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Forward Deplyed Engineering Certification Training Program

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Forward Deplyed Engineering Certification Training Program

Forward Deployed Engineering Program

Build enterprise-ready AI agents with hands-on, forward-deployed engineering skills.

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Learn end-to-end AI application development using Python and Generative AI.
Build AI agents, RAG applications, and multi-agent systems through hands-on labs.
Work with LangChain, LangGraph, vector databases, FastAPI, Docker, and cloud platforms.
Develop practical skills through labs, assessments, assignments, and a capstone project.
Prepare for enterprise AI projects and AI-focused technical interviews.

Our Key to Exam and Career Success

Guaranteed to Run Workshops

Expert-Led Study Sessions

Networking Opportunities

Flexible Monthly Payment Plans

Real-World Case Studies

Hands-On Project Experience

Group/Corporate Training

Empower teams through training

skill up your team

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Course overview Benefits Prerequisites Curriculum FAQs Contact Course Advisor

Course Overview

Overview of the Forward Deployed Engineering Program

The Forward Deployed Engineering Program offered by Simpliaxis is designed for software engineers, developers, testers, architects, and other technical professionals. The curriculum of this training program embeds Python, machine learning, Generative AI, AI agents, Retrieval-Augmented Generation (RAG), and deployment practices into a structured learning path focusing on practical application. Through 60 hours of instructor-led sessions, hands-on labs, assessments, assignments, and a capstone project, participants build AI-powered applications using modern frameworks and development tools.

What is the Forward Deployed Engineering Program?

The Forward Deployed Engineering Program is a specialized training course that helps professionals to learn the ways to build, evaluate, and deploy AI-powered applications throughout the complete development lifecycle. By covering modern AI frameworks, deployment practices, and production workflows, this live virtual training program allows participants to develop the technical capabilities for creating enterprise-ready AI solutions.

What Is Included in the Forward Deployed Engineering Program?

The Forward Deployed Engineering Program covers the complete workflow of developing, deploying, and evaluating AI-powered applications. This includes

60 hours of instructor-led training
Hands-on labs and practical exercises
Take-home assignments and quizzes
Mid-term assessment
Capstone AI agent development project

What Skills are Covered in the Forward Deployed Engineering Program?

The Forward Deployed Engineering program helps participants in developing the following skills through hands-on learning across the complete AI application lifecycle.

Python programming
Object-oriented programming
Machine learning model development and evaluation
Data preprocessing and exploratory data analysis
Prompt engineering and LLM fundamentals
Retrieval-Augmented Generation (RAG) applications
AI agent design and development
Multi-agent workflow implementation
Tool integration and function calling
AI model evaluation and observability
Responsible AI and AI governance
API development using FastAPI
Containerization with Docker
AI application deployment and cloud serving
AI project lifecycle understanding

What Tools and Environment are Used in the Forward Deployed Engineering Program?

Participants who enroll in the training program gain hands-on experience with the following industry-standard tools and development environments:

Programming Language

Python

AI & Machine Learning Libraries

NumPy
Pandas
Matplotlib
Scikit-learn
TensorFlow
Keras
NLTK
spaCy

Generative AI Models

OpenAI GPT Models
Anthropic Claude Models
Google Gemini

AI Frameworks

LangChain
LangGraph
LlamaIndex

Vector Databases

FAISS
Pinecone
Weaviate
PostgreSQL (pgvector)

Development & Deployment

FastAPI
Docker
Redis

Evaluation & Observability

LangSmith
Langfuse

Cloud Platforms

AWS Bedrock AgentCore
Azure OpenAI
Azure AI Foundry

Search & Web Data Access

Tavily
Serper
DuckDuckGo
Firecrawl

AI-Assisted Development

GitHub Copilot

What are the Modes of the Forward Deployed Engineering Program?

We provide the Forward Deployed Engineering program in different flexible learning modes to meet the training needs of both individual professionals and organizations. This includes:

Instructor-Led Live Online Training: Interactive live sessions led by experienced instructors, complemented by hands-on labs, practical assignments, assessments, and a capstone project.

Corporate Training: Customized training programs designed for organizations to upskill software engineers, developers, testers, architects, and other technical teams.

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Forward Deployed Engineering Training Highlights

What are the Key Highlights of the Forward Deployed Engineering Program?

The forward-deployed engineering program offered by Simpliaxis comes with the following competitive features:

60 hours of structured instructor-led technical training.

A comprehensive curriculum covering Python, machine learning, Generative AI, RAG, etc.

Extensive hands-on labs, take-home assignments, quizzes, and assessments.

Practical capstone project focused on building an industry-agnostic AI agent.

Training on modern AI frameworks, vector databases, deployment tools, and cloud platforms.

Coverage of Responsible AI, agent evaluation, observability, and monitoring.

End-to-end AI application development, from model development to cloud deployment.

Exposure to enterprise AI development tools, including LangChain, LangGraph, FastAPI, Docker, Redis, and pgvector.

Interview-oriented content designed to prepare learners for AI-focused technical roles.

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Career upliftment

Boost your career with the Forward Deployed Engineering Program

What are the Learning Outcomes of the Forward Deployed Engineering Program?

By the end of this training program, participants will be able to:

Build end-to-end AI applications using Python, Generative AI, LLMs, and AI development frameworks.
Develop Retrieval-Augmented Generation (RAG) applications and AI agents using modern tools and vector databases.
Assess, monitor, optimize, and secure AI applications using responsible AI and observability practices.
Deploy AI agents and RAG applications using FastAPI, Docker, and cloud platforms such as AWS and Azure.
Apply practical AI development skills through hands-on labs, assessments, and a capstone project while preparing for AI-focused technical roles.

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Forward Deployed Engineering Practice Test

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Sample Papers
Time-Limited
Comprehensive Explanation
Previous Exams

Benefits focused on Individuals and corporate

What are the Benefits of the Forward Deployed Engineering Program?

Individuals Benefits

Gain End-to-End AI Development Skills: Professionals who enroll in this program build practical expertise across the complete AI application development lifecycle.
Learn Through Hands-on Practice: The forward deployed engineering program develops technical knowledge of professionals through extensive labs, assignments, and a capstone project.
Build Enterprise AI Applications: Professionals improve their expertise and practical experience inAI agents, RAG applications, and production-ready AI solutions.
Enhance AI Technical Skills: The FDE program expands the expertise of professionals in Python, machine learning, Generative AI, deployment, and AI evaluation.
Become Project-Ready: Professionals who enroll in this program develop implementation skills required to contribute effectively to enterprise AI projects.

Corporate Benefits

Upskill Technical Teams: The FDE program allows software engineering teams to build modern AI-powered applications using industry-relevant technologies.
Improve AI Development Capabilities: Enterprises, with the help of the Forward deployed engineering program, developpractical knowledge of their team in AI agents, RAG, deployment, and production workflows.
Accelerate Enterprise AI Adoption: Businesses improve internal expertise required for developing and deploying enterprise AI solutions through the FDE program.
Standardize AI Development Practices: The Forward deployed engineering program train enterprise teams on structured AI development, evaluation, monitoring, and deployment methodologies.
Build Hands-on Implementation Skills: Businesses ensure practical learning through instructor-led sessions, labs, assignments, and projects.

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Common attendees

Who usually attend Forward Deployed Engineering Training

The training program is designed for the following professionals who want to

Software Engineers
Software Developers
Software Testing Professionals
Architects
Other Technical Personas

Prerequisites for Forward Deployed Engineering certification training

What are the Prerequisites for the Forward Deployed Engineering Program

The Forward Deployed Engineering Program is designed for professionals with a foundational understanding of software development and modern engineering practices. However, to improve the learning outcomes and take the most benefit from the hands-on labs, deployment simulations, and enterprise-focused exercises, participants should have:

Proficiency in at least one general-purpose programming language (Python, Java, TypeScript, or similar)
Working knowledge of REST APIs, SQL, and Git
Basic familiarity with cloud platforms (AWS, GCP, or Azure) and containers (Docker)

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Exam Format

What are the Assessment & Grading Criteria for the Forward Deployed Engineering Program?

Module Labs (40%) – Hands-on assessments in integration, data engineering, deployment, and debugging.
Discovery Workshop & Written SOW (15%) – Evaluation of customer discovery and Statement of Work preparation.
Stakeholder Communication Exercise (10%) – Assessment of technical communication and stakeholder management skills.
Capstone Deployment Simulation (30%) – Team-based end-to-end enterprise deployment project.
Participation (5%) – Active involvement in labs, discussions, and instructor-led sessions.

What are the Steps to Complete the Forward Deployed Engineering Program?

Certification Process for Forward Deployed Engineering Program

Attend Training:

Participate in the live training sessions and learn the complete curriculum covering Python, machine learning, Generative AI, AI agents, RAG, and deployment.

Complete Hands-on Labs and Assignments:

Apply the concepts through practical labs, take-home exercises, and assignments designed to reinforce technical skills.

Complete Assessments:

Demonstrate your understanding by completing quizzes, the midterm assessment, and other evaluation activities included in the program.

Build the Capstone Project:

Develop an industry-agnostic AI agent by applying the concepts and tools covered throughout the training, showcasing practical implementation skills.

Curriculum

Module 1: Python Basics (4 Hours)

Variables, data types, strings, numbers
Lists, tuples, dictionaries, sets
Conditions and loops
Functions and return values, OOPS
HTTP/HTTPS requests using Python
Virtual environment and package install
Lab: Python features & writing logic

Module 2: Machine Learning Using Python (18 Hours)

Machine learning basics, including ML types
Exploratory data analysis, data cleaning /transformation, feature extraction
Python libraries/packages—sklearn, numpy, os, sys, matplotlib, pandas
Lab: Exploratory data analysis
Take-home Exercises
Regression basics, examples of Regression use cases
Overview of Regression Algorithms
Lab: Regression
Take-home Assignment
Classification basics, examples of Classification use cases
Overview of Classification Algorithms
Lab: Classification
Take-home Assignment
Basics of Clustering & Dimensionality Reduction and where to use them/use cases
Midterm Assessment/Test-1
Overview of Clustering and Dimensionality Reduction techniques
Lab: Clustering
Lab: Dimensionality Reduction
Take-home assignment
Deep Learning & Deep Neural Networks
Tensorflow & keras for working with Deep neural networks
Lab: Building Deep neural network with Tensorflow
Lab: Working with Keras for Deep learning
Take-home Assignment
Neurons, neural network, activation function, batch, epochs
Take-home Assignment
Natural Language Processing & NLP Pipeline
NLP libraries/ packages - NLTK, Spacy
Lab: Working with NLP Techniques
Take-home Assignment
Concepts of Model evaluation and testing (incl. role of software testers in ML projects)
Python libraries/ packages for model evaluation
Lab: Evaluating Classification model
Take-home Exercises
Deploying ML Models on the cloud (as part of MLOps Lifecycle)
AI project lifecycle

Module 3: Introduction to Generative AI and LLMs (4 Hours)

Introduction to Artificial Intelligence (AI), Generative AI & General AI
Natural Language Processing (NLP) Fundamentals
Overview of Large Language Models (LLMs) & Multimodal LLMs
State-of-the-Art LLMs: Anthropic Claude Models, OpenAI GPT Models and Google Gemini
How LLMs Work
Tokenization in LLMs
Context Window
LLM Hyperparameters: Temperature, Max Tokens, Top-p & Sampling Controls
LLM Consistency, Probabilistic & Non-deterministic Behavior
Prompt Structure & Roles
Prompt Engineering Techniques
Introduction to Fine-tuning LLMs & SLMs
Lab: Simple LLM API Call
Lab: Subword Tokenization in LLMs

Module 4: Vector Databases and Embeddings (4 Hours)

Introduction to Embeddings and Vector Representations
Vector Database Fundamentals: FAISS, Pinecone, Weaviate
Indexing Strategies, Similarity Search & Vector Search Techniques
Metadata Filtering and Hybrid Search Basics
Choosing the Right Vector DB for Production Use Cases
Lab: Setting up and querying FAISS / Pinecone / Weaviate

Module 5: Working with RAG (6 Hours)

Introduction to Retrieval Augmented Generation (RAG)
RAG Architecture and End-to-End Flow
Business Use Cases of RAG Applications and Vertical AI Agents
Document Loading and Data Ingestion Pipelines
Chunking Strategies and Chunk Optimization
Retrieval Mechanics, Similarity Search and Vector Search Techniques
Techniques for Improving RAG Accuracy, Reranking, Query Optimization, Chunk Optimization and Retrieval Tuning
Agentic RAG, Retrieval Planning, Query Rewriting, iterative retrieval, and Answer Refinement
Hybrid RAG: Sparse Retrieval, Dense Retrieval, Fusion Techniques, Metadata Filtering, and Reranking. Knowledge Graph
LangChain + LlamaIndex for RAG Development
Introduction to PostgreSQL:- pgvector
PostgreSQL for RAG (pgvector): Storing Documents, Structured Outputs and Conversation History
RAG Evaluation: Relevance, Grounding, Faithfulness and Test Set Creation
Lab: Agentic RAG
Lab: Graph RAG
Lab: Hybrid RAG
Lab: RAG Evaluation

Module 6: Basics of AI Agents (6 Hours)

Introduction to AI Agents, Agentic AI, and Role of LLMs / SLMs
AI Agent Components and Agent Architecture
Agentic Workflows, Planning, Reflection and Reasoning in AI Agents
Prompt Engineering and Context Engineering for AI Agents
Agent State, Memory and Context Handling Fundamentals
Tools and Tool Usage in AI Agents
Tool Types, Function Calling and API Integrations
Business and Technical Use Cases of AI Agents including Vertical AI Agents
Introduction to LangChain for AI Agent Development
Lab: Building Basic AI Agents with LangChain
Building AI Agents with Tools and Function Calling using LangChain
Lab: Tool Integration with LangChain
Designing AI Agent Specifications and Conversational Workflows

Module 7: Technical Development of AI Agents (8 Hours)

Advanced Agentic Workflows and Multi-agent System Design
Supervisor-worker Pattern, Agent Collaboration and Task Delegation
Graph-based Multi-agent Systems with LangGraph
Graph State, Nodes, Edges, Branching and Retries
Lab: Multi-agent System with LangGraph
Google ADK for building multi-agent systems
Lab: Google ADK Multi-agent System
Agent Memory: Short-term, Long-term, and Stateful Agents
Introduction to Redis
Redis for Agent Memory: Session Management, Semantic Caching and Latency Reduction
Lab: Stateful Agent Memory with Redis and LangGraph
Real-time Web Data Access with Tavily/Serper/duckduckgo/Firecrawl
Model Context Protocol (MCP): Architecture and Tool Integration Overview (Conceptual)
Capstone Project: Industry-Agnostic AI Agent — Choose from Customer Support Agent, Process Automation Agent, Advisory/Recommendation Agent, or Operations Monitoring Agent (adaptable to any domain)

Module 8: Responsible AI, Agent Evaluation & Monitoring (4 Hours)

Responsible AI, AI Governance and Safety for AI Agents
Agent Evaluation Fundamentals, Task Success, Tool Correctness, Multi-step Reliability and Failure Analysis
Evaluating AI Agents in Production
LLM-as-a-Judge and Automated Agent Evaluation Techniques
Lab: LLM-as-a-Judge for Agent Evaluation
Agent Observability, LangSmith Tracing, Prompt Inspection, Tool Inspection and Debugging
Lab: Agent Observability with LangSmith
Lab: Agent Evaluation with Langfuse
Guardrails for AI Agents, Input Validation, Output Validation, Topic Restriction and Fallback Handling
Agent Security, Secure Tool Usage and MCP Security Best Practices
Reducing Hallucinations, Failure Management and Debugging AI Agents in Production
Token Economics, Cost Optimization, Semantic Caching and Latency Reduction
Post-production Considerations for AI Agents and RAG Applications
Cross-Industry AI Use Cases & Compliance: Domain Context for Deployments Across Sectors (e.g., BFSI, Healthcare, Retail, Manufacturing, IT/ITES)
Real-world Scenarios — Agent Evaluation

Module 9: AI Agent Deployment & Serving (6 Hours)

Introduction to AI Agent Deployment and AgentOps
Serving AI Agents and RAG Applications as API Endpoints
FastAPI Fundamentals, App Setup, Routing, Request and Response Models
Building GET and POST Endpoints for AI Agents and RAG Systems
Lab: Serving AI Agent with FastAPI
Containerization of AI Agents using Docker (Hands-on)
Lab: Dockerizing and Running an AI Agent
CI/CD Pipelines for AI Agent Deployment
Lab: CI/CD Pipeline for an AI Agent
Cloud Deployment Hands-on: AWS Bedrock AgentCore
Cloud Deployment Hands-on: Azure OpenAI + Azure AI Foundry
Lab: End-to-end Cloud Deployment (AWS or Azure)
Deployment & Agentic AI services in AWS, GCP, and Azure (demo/walkthrough)
Deployment and Scaling Fundamentals for AI Agents and RAG Applications

Course Curriculum

What is the Syllabus for the Forward Deployed Engineering Program?

Download Curriculum

Variables, data types, strings, numbers
Lists, tuples, dictionaries, sets
Conditions and loops
Functions and return values, OOPS
HTTP/HTTPS requests using Python
Virtual environment and package install
Lab: Python features & writing logic

Machine learning basics, including ML types
Exploratory data analysis, data cleaning /transformation, feature extraction
Python libraries/packages—sklearn, numpy, os, sys, matplotlib, pandas
Lab: Exploratory data analysis
Take-home Exercises
Regression basics, examples of Regression use cases
Overview of Regression Algorithms
Lab: Regression
Take-home Assignment
Classification basics, examples of Classification use cases
Overview of Classification Algorithms
Lab: Classification
Take-home Assignment
Basics of Clustering & Dimensionality Reduction and where to use them/use cases
Midterm Assessment/Test-1
Overview of Clustering and Dimensionality Reduction techniques
Lab: Clustering
Lab: Dimensionality Reduction
Take-home assignment
Deep Learning & Deep Neural Networks
Tensorflow & keras for working with Deep neural networks
Lab: Building Deep neural network with Tensorflow
Lab: Working with Keras for Deep learning
Take-home Assignment
Neurons, neural network, activation function, batch, epochs
Take-home Assignment
Natural Language Processing & NLP Pipeline
NLP libraries/ packages - NLTK, Spacy
Lab: Working with NLP Techniques
Take-home Assignment
Concepts of Model evaluation and testing (incl. role of software testers in ML projects)
Python libraries/ packages for model evaluation
Lab: Evaluating Classification model
Take-home Exercises
Deploying ML Models on the cloud (as part of MLOps Lifecycle)
AI project lifecycle

Introduction to Artificial Intelligence (AI), Generative AI & General AI
Natural Language Processing (NLP) Fundamentals
Overview of Large Language Models (LLMs) & Multimodal LLMs
State-of-the-Art LLMs: Anthropic Claude Models, OpenAI GPT Models and Google Gemini
How LLMs Work
Tokenization in LLMs
Context Window
LLM Hyperparameters: Temperature, Max Tokens, Top-p & Sampling Controls
LLM Consistency, Probabilistic & Non-deterministic Behavior
Prompt Structure & Roles
Prompt Engineering Techniques
Introduction to Fine-tuning LLMs & SLMs
Lab: Simple LLM API Call
Lab: Subword Tokenization in LLMs

Introduction to Embeddings and Vector Representations
Vector Database Fundamentals: FAISS, Pinecone, Weaviate
Indexing Strategies, Similarity Search & Vector Search Techniques
Metadata Filtering and Hybrid Search Basics
Choosing the Right Vector DB for Production Use Cases
Lab: Setting up and querying FAISS / Pinecone / Weaviate

Introduction to Retrieval Augmented Generation (RAG)
RAG Architecture and End-to-End Flow
Business Use Cases of RAG Applications and Vertical AI Agents
Document Loading and Data Ingestion Pipelines
Chunking Strategies and Chunk Optimization
Retrieval Mechanics, Similarity Search and Vector Search Techniques
Techniques for Improving RAG Accuracy, Reranking, Query Optimization, Chunk Optimization and Retrieval Tuning
Agentic RAG, Retrieval Planning, Query Rewriting, iterative retrieval, and Answer Refinement
Hybrid RAG: Sparse Retrieval, Dense Retrieval, Fusion Techniques, Metadata Filtering, and Reranking. Knowledge Graph
LangChain + LlamaIndex for RAG Development
Introduction to PostgreSQL:- pgvector
PostgreSQL for RAG (pgvector): Storing Documents, Structured Outputs and Conversation History
RAG Evaluation: Relevance, Grounding, Faithfulness and Test Set Creation
Lab: Agentic RAG
Lab: Graph RAG
Lab: Hybrid RAG
Lab: RAG Evaluation

Introduction to AI Agents, Agentic AI, and Role of LLMs / SLMs
AI Agent Components and Agent Architecture
Agentic Workflows, Planning, Reflection and Reasoning in AI Agents
Prompt Engineering and Context Engineering for AI Agents
Agent State, Memory and Context Handling Fundamentals
Tools and Tool Usage in AI Agents
Tool Types, Function Calling and API Integrations
Business and Technical Use Cases of AI Agents including Vertical AI Agents
Introduction to LangChain for AI Agent Development
Lab: Building Basic AI Agents with LangChain
Building AI Agents with Tools and Function Calling using LangChain
Lab: Tool Integration with LangChain
Designing AI Agent Specifications and Conversational Workflows

Advanced Agentic Workflows and Multi-agent System Design
Supervisor-worker Pattern, Agent Collaboration and Task Delegation
Graph-based Multi-agent Systems with LangGraph
Graph State, Nodes, Edges, Branching and Retries
Lab: Multi-agent System with LangGraph
Google ADK for building multi-agent systems
Lab: Google ADK Multi-agent System
Agent Memory: Short-term, Long-term, and Stateful Agents
Introduction to Redis
Redis for Agent Memory: Session Management, Semantic Caching and Latency Reduction
Lab: Stateful Agent Memory with Redis and LangGraph
Real-time Web Data Access with Tavily/Serper/duckduckgo/Firecrawl
Model Context Protocol (MCP): Architecture and Tool Integration Overview (Conceptual)
Capstone Project: Industry-Agnostic AI Agent — Choose from Customer Support Agent, Process Automation Agent, Advisory/Recommendation Agent, or Operations Monitoring Agent (adaptable to any domain)

Responsible AI, AI Governance and Safety for AI Agents
Agent Evaluation Fundamentals, Task Success, Tool Correctness, Multi-step Reliability and Failure Analysis
Evaluating AI Agents in Production
LLM-as-a-Judge and Automated Agent Evaluation Techniques
Lab: LLM-as-a-Judge for Agent Evaluation
Agent Observability, LangSmith Tracing, Prompt Inspection, Tool Inspection and Debugging
Lab: Agent Observability with LangSmith
Lab: Agent Evaluation with Langfuse
Guardrails for AI Agents, Input Validation, Output Validation, Topic Restriction and Fallback Handling
Agent Security, Secure Tool Usage and MCP Security Best Practices
Reducing Hallucinations, Failure Management and Debugging AI Agents in Production
Token Economics, Cost Optimization, Semantic Caching and Latency Reduction
Post-production Considerations for AI Agents and RAG Applications
Cross-Industry AI Use Cases & Compliance: Domain Context for Deployments Across Sectors (e.g., BFSI, Healthcare, Retail, Manufacturing, IT/ITES)
Real-world Scenarios — Agent Evaluation

Introduction to AI Agent Deployment and AgentOps
Serving AI Agents and RAG Applications as API Endpoints
FastAPI Fundamentals, App Setup, Routing, Request and Response Models
Building GET and POST Endpoints for AI Agents and RAG Systems
Lab: Serving AI Agent with FastAPI
Containerization of AI Agents using Docker (Hands-on)
Lab: Dockerizing and Running an AI Agent
CI/CD Pipelines for AI Agent Deployment
Lab: CI/CD Pipeline for an AI Agent
Cloud Deployment Hands-on: AWS Bedrock AgentCore
Cloud Deployment Hands-on: Azure OpenAI + Azure AI Foundry
Lab: End-to-end Cloud Deployment (AWS or Azure)
Deployment & Agentic AI services in AWS, GCP, and Azure (demo/walkthrough)
Deployment and Scaling Fundamentals for AI Agents and RAG Applications

Download Curriculum

Have more questions or need

personalized guidance?

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What you will learn in this Forward Deployed Engineering workshop

What are the Learning Objectives of the Forward Deployed Engineering Program?

Build Strong Python and Machine Learning Foundations

Improve proficiency in Python programming, data analysis, machine learning algorithms, and deep learning concepts required for modern AI application development.

Learn Generative AI and Large Language Models

Understand the fundamentals of Generative AI, LLMs, prompt engineering, tokenization, fine-tuning concepts, and multimodal AI models.

Develop AI Agents and RAG Applications

Gain practical experience in building AI agents, Retrieval-Augmented Generation (RAG) systems, and multi-agent workflows using modern AI frameworks.

Master AI Deployment and Production Workflows

Learn the ways to deploy AI applications using APIs, Docker, cloud platforms, and enterprise deployment practices.

Evaluate and Optimize AI Applications

Understand responsible AI, agent evaluation, observability, security, debugging, and performance optimization techniques.

Forward Deployed Engineering Certification Course For Corporates

Why Corporate Choose Us for Forward Deployed Engineering Programs

Organizations across domains look for practical AI training for their technical teams so that they can develop, deploy, and manage enterprise AI applications effectively. At Simpliaxis, we come a step forward to meet these requirements of businesses with our structured instructor-led training with extensive hands-on practice. Here are the key reasons why corporate prefer us for the program

Customized corporate training tailored to organizational learning objectives.
Instructor-led learning with interactive technical sessions.
Comprehensive curriculum covering the complete AI application lifecycle.
Hands-on labs, assignments, and a capstone project for practical skill development.
Training on industry-relevant AI frameworks, vector databases, and cloud platforms.
Focus on building production-ready AI application development capabilities.
Interview-oriented and project-ready learning outcomes for technical teams.

Corporate Group enquiry

Our Customer words for us

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What is Forward Deployed Engineering?

Forward-deployed engineering is an approach that focuses on building, deploying, and supporting technology solutions in customer or enterprise environments. This program prepares participants to develop AI-powered applications using Python, Generative AI, AI agents, RAG, and modern deployment practices across the complete application lifecycle.

Who should enroll in the Forward Deployed Engineering program?

The FDE training program is suitable for software engineers, solutions architects, implementation engineers, and engineers working in customer deployment roles.

How long is the Forward Deployed Engineering program?

The Forward Deployed Engineering Program is a 40-hour, live, instructor-led training. The curriculum is divided into 10 modules, with each module allocated a specific duration to ensure comprehensive coverage of the topics.

What are the prerequisites to enroll in the Forward Deployed Engineering training program?

Participants who want to enroll in this training program should have proficiency in at least one programming language and a working knowledge of REST APIs, SQL, and Git. Also, they should have a basic familiarity with cloud platforms such as AWS, GCP, or Azure and containerization using Docker to successfully complete the program.

Is the Forward Deployed Engineering training practical?

Yes, this training program is completely practical. It includes hands-on labs covering integration, deployment, debugging, data engineering, and an enterprise deployment simulation.

What will I learn by enrolling in the Forward Deployed Engineering Program?

By enrolling in this training program, you will learn about customer discovery, solution architecture, systems integration, deployment, data engineering, stakeholder communication, production support, and enterprise deployment practices.

Does the FDE training program include a capstone project?

Yes, participants who enroll in the training and cover all the course modules will have to complete a team-based end-to-end deployment simulation using a simulated enterprise client scenario.

How are participants assessed?

Participants are usually assessed through module labs, a discovery workshop with a written Statement of Work (SOW), a stakeholder communication exercise, a team-based end-to-end deployment simulation, and overall participation in labs, discussions, and instructor-led office hours.

What tools are used during Forward Deployed Engineering training?

During the FDE training, participants use Git, GitHub or GitLab, AWS or GCP, Docker, Postman, SQL, Airflow (or similar), webhook testing tools, and collaboration platforms.

What happens to my token or partial payment if I don’t enroll immediately?

Token or partial payments will become non-refundable after 15 days from the date of payment.
Any token or partial amount paid can be applied towards enrollment for any available sessions on our website within 6 months of the payment date.

Is there a preferred currency for payment, especially for international students?

While we accept payments in various currencies, we recommend using the currency from where you want to attend the course.

Can payments be made online through your website, or do I need to visit a physical location?

Payments can be made conveniently through our secure online payment portal on our website. We do not require visiting a physical location for payment.

Do you offer any discounts or promotions for early payment?

Yes, we offer discounts and promotions for early payments or certain corporate Training. Keep an eye on our website or contact us to get exciting offers.

Can I pay for the course in installments, or is full payment required upfront?

You have the flexibility to pay for the course either in full upfront or in installments, depending on your preference. To know more details about it contact us.

Will I get a refund if I cancel my enrollment?

No, refund is not possible. You can reschedule your course. To know more about refunds, visit our Refund Policy.

Where will I get my payment receipt?

Upon successful completion of payment, you will receive a confirmation email of your payment along with payment invoices.

Is there any transaction or processing fee?

No, there is no transaction processing fee.

What cards do you accept?

We accept payments from VISA, Master, Maestro, AMEX, and Discover.

What payment options are available?

All types of payment methods are available. You can pay Online through Credit Cards, Debit Cards and Net Banking, Direct Bank Transfer/Wire Transfer/Wallet to our account and many more.Other payment options available are stripe, Splitit, PayPal for US & CA regions.

What if I miss a class? Are there any money back options?

No, you won't get your money back. But, as per your convenience, we can reschedule your training without any extra charges.

If I want to know more about Training, whom should I connect with?

If you want to know more about the course you can connect us in mail hello@simpliaxis.com or you can call us.

Is there any option to complete the Training in the native language if a participant chooses to?

English is the preferred language for training delivery. For corporate Training, any language other than English will have to be requested as a custom service. However, this will be subject to additional costs and the availability of a native language trainer. Please note that only official languages will be considered for this request.

Can I receive personalized Training at my convenience?

Yes, we’ll be posting multiple schedules every week for all the courses. Based on your convenience, you can select the training schedule. Also, we’ve E-Learning programs for popular courses, you can enroll and watch the training modules whenever you want.

Where do I find the upcoming schedules of my course?

Select the course of your interest from the website and view all the upcoming workshop schedules. You can select your course from the website https://www.simpliaxis.com/

After enrollment, can I change the date of my training class?

Yes, you can change the date, but you have to inform it 3-4 days before the Scheduled date.

Do I get any certificate upon completion of the course?

Yes, you will receive a certificate after successful completion of the training course.

How do I enroll in the training course?

Choose the course of your interest, schedule it at your convenience from our upcoming schedule list, and register for the training by submitting your details. Proceed to make a payment and you will receive a confirmation email of your course enrolment. Or you can connect our career advisors to consult & choose your course.

What are the different courses offered by Simpliaxis?

At Simpliaxis we provide training for courses of all the in-demand technologies. One can choose a course from Project Management, Agile, IT, DevOps, Business, IT Service Management, Quality Management, etc based on their choice of interest. To know more, please visit https://www.simpliaxis.com/

What are the different modes of Training available for Simpliaxis courses?

We provide different modes of Training: virtual Online Training, Live Virtual Training, Corporate Training & also E-learning.

Do you offer online Training?

Yes, For every course, online Training is available.

Do you offer corporate Training?

Yes, we do provide corporate Training. We customize Training based on our clients' needs.

Who are the instructors of my course?

All our trainers are highly qualified and certified, having numerous years of experience in the training industry. You can choose your instructors for your course.

Is there any discount available for the Simpliaxis courses?

Yes, we do provide discounts on multiple courses. Please visit the schedules page to view the available discounts.

Whom do I contact if I have more queries regarding my course?

You can reach out to our experienced career counselor
or email us on support@simpliaxis.com
or contact any of our phone numbers
or you can chat with us anytime
or visit our contact us page https://www.simpliaxis.com/contact-us

Are your courses affordable?

Yes, course fees for all technologies are affordable. Along with that, we do provide discounts on various courses.

Why should I choose Simpliaxis?

Simpliaxis offers personalized corporate training programs designed for enterprise teams seeking to refine their employees' skills in various job processes or roles, whether through in-person sessions or live virtual instruction. Led by our skilled and certified trainers, these certification programs not only elevate learning curves but also empower teams to effectively apply their newfound skills to meet industry benchmarks. With over 40 certification programs available, the impact is profound, evident in a remarkable 45% improvement in employee retention rates. On average, there's been a notable 30% enhancement in both work satisfaction and capabilities, showcasing the tangible benefits of our training initiatives.

What is the object of Simpliaxis?

To provide high-quality training and help our professional participants & clients to build a high-performing workforce.

How much discount will I get if I enroll for the Training?

It totally depends upon the course. Please check our website regularly for new offers and discounts throughout the year. You can also chat with one of our career consultants to see if any discounts are available.

Forward Deployed Engineering Course FAQs

Forward Deployed Engineering Program FAQs

The FDE training program is suitable for software engineers, solutions architects, implementation engineers, and engineers working in customer deployment roles.

Yes, this training program is completely practical. It includes hands-on labs covering integration, deployment, debugging, data engineering, and an enterprise deployment simulation.

Yes, participants who enroll in the training and cover all the course modules will have to complete a team-based end-to-end deployment simulation using a simulated enterprise client scenario.

During the FDE training, participants use Git, GitHub or GitLab, AWS or GCP, Docker, Postman, SQL, Airflow (or similar), webhook testing tools, and collaboration platforms.

Token or partial payments will become non-refundable after 15 days from the date of payment.
Any token or partial amount paid can be applied towards enrollment for any available sessions on our website within 6 months of the payment date.

While we accept payments in various currencies, we recommend using the currency from where you want to attend the course.

Payments can be made conveniently through our secure online payment portal on our website. We do not require visiting a physical location for payment.

Yes, we offer discounts and promotions for early payments or certain corporate Training. Keep an eye on our website or contact us to get exciting offers.

You have the flexibility to pay for the course either in full upfront or in installments, depending on your preference. To know more details about it contact us.

No, refund is not possible. You can reschedule your course. To know more about refunds, visit our Refund Policy.

Upon successful completion of payment, you will receive a confirmation email of your payment along with payment invoices.

No, there is no transaction processing fee.

We accept payments from VISA, Master, Maestro, AMEX, and Discover.

No, you won't get your money back. But, as per your convenience, we can reschedule your training without any extra charges.

If you want to know more about the course you can connect us in mail hello@simpliaxis.com or you can call us.

Select the course of your interest from the website and view all the upcoming workshop schedules. You can select your course from the website https://www.simpliaxis.com/

Yes, you can change the date, but you have to inform it 3-4 days before the Scheduled date.

Yes, you will receive a certificate after successful completion of the training course.

We provide different modes of Training: virtual Online Training, Live Virtual Training, Corporate Training & also E-learning.

Yes, For every course, online Training is available.

Yes, we do provide corporate Training. We customize Training based on our clients' needs.

All our trainers are highly qualified and certified, having numerous years of experience in the training industry. You can choose your instructors for your course.

Yes, we do provide discounts on multiple courses. Please visit the schedules page to view the available discounts.

Yes, course fees for all technologies are affordable. Along with that, we do provide discounts on various courses.

To provide high-quality training and help our professional participants & clients to build a high-performing workforce.

Find out why 1,000+ professionals love SimpliAxis

Daniel Harper
Product Manager
Outstanding training from Simpliaxis! The curriculum on multi-agent architectures and RAG pipeline development using Claude was thorough and current. Real-world use cases across industries gave me the confidence to lead AI initiatives at my organization immediately.
Sarah Mitchell
Cloud AI Architect
I enrolled in this course to upskill as a software architect, and Simpliaxis exceeded my expectations. The MCP server integration labs and Claude API demos were practical and industry-relevant. The trainer's mentorship made complex agentic concepts easy to grasp.
Priya Venkataraman
AI Solutions Architect
The hands-on labs in Simpliaxis's Agentic AI Engineering course were brilliant. From context engineering to building RAG applications with Langflow, every module was structured and practical. The trainers were knowledgeable and always available for 1:1 support.
James O'Brien
Technical Lead
Simpliaxis's Agentic AI Engineering with Claude course was a game-changer. The 48-hour live sessions covering RAG, LangChain, and multi-agent systems were incredibly hands-on. I went from theory to deploying a production-grade AI agent within weeks. Highly recommended!
Arjun Krishnamurthy
AI/ML Engineer
Simpliaxis delivered exceptional value through this Agentic AI Engineering course. Building autonomous agents with Claude SDK, integrating MCP servers, and deploying on cloud platforms gave me skills that no other training provided. My career in AI has taken a huge leap forward.
Jai Kumar Jadhav
I’m happy to share that I’ve obtained a new certification: Certified Scrum Product Owner (CSPO) from Scrum Alliance🙏 A heartfelt thank you to Simpliaxis for a well-structured training experience and to our amazing Naveen Nanjundappa for thoroughly covering the CSPO concepts and making the learning engaging and practical

Why Choose Simpliaxis for Forward Deployed Engineering Program?

Learn from Experienced Instructors

At Simpliaxis, we deliver the FDE program with the help of experienced professionals who simplify complex AI concepts and guide learners through every stage of the curriculum.

Comprehensive End-to-End Curriculum

We cover the complete AI application lifecycle, from Python and machine learning to AI agents, RAG, deployment, evaluation, and production workflows.

Hands-on Learning Approach

Our trainers strengthen your understanding of the concepts through practical labs, take-home assignments, assessments, and a capstone project that emphasizes real-world implementation.

Industry-Relevant AI Technologies

Our FRD program covers widely used AI frameworks, vector databases, cloud platforms, and deployment tools throughout the training.

Structured Learning Path

We progress the training through a well-organized learning path that builds foundational knowledge before advancing to enterprise AI development concepts.

Enterprise-Focused Skill Development

At Simpliaxis, we help professionals to develop practical skills required to build, deploy, monitor, and evaluate AI-powered applications for enterprise environments.

Interview-Oriented Preparation

Our hands-on training strengthens your readiness for AI-focused technical roles through quizzes, assessments, and interview-oriented learning outcomes.

Practical Capstone Project

Simpliaxis helps you to apply your learning by developing an industry-agnostic AI agent that demonstrates your ability to implement end-to-end AI solutions.

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What are the job opportunities for a Forward Deployed Engineer?

Forward deployed engineers have diverse career opportunities across roles such as forward deployed engineer, solutions engineer, cloud engineer, integration engineer, DevOps engineer, and customer success engineer. They play a critical role in connecting technical solutions with real business requirements by working directly with customers, designing scalable solutions, integrating enterprise systems, managing deployments, and resolving complex technical challenges. With expertise in software engineering, cloud technologies, APIs, data workflows, and customer collaboration, FDEs can build rewarding careers in technology companies, AI organizations, consulting firms, and enterprise digital transformation teams.

What Certifications are Required for the Forward Deployed Engineering Program?

The Forward Deployed Engineering Program is designed for professionals with a foundational understanding of software development and modern engineering practices. To gain maximum value from hands-on labs, deployment simulations, and enterprise-focused exercises, participants should have:

Programming Knowledge: Proficiency in at least one general-purpose programming language such as Python, Java, or TypeScript.
Development Fundamentals: Working knowledge of REST APIs, SQL, and Git for software development and integration tasks.
Cloud & Container Basics: Basic familiarity with cloud platforms like AWS, GCP, or Azure, along with container technologies such as Docker.

Disclaimer : Certified Scrum Master(CSM®),Advanced Certified Scrum Master(A-CSM®), Certified Scrum Professional ScrumMaster(CSP-SM®), Certified Scrum Product Owner (CSPO®), Advanced Certified Scrum Product Owner (A-CSPO®), Certified Scrum Professional Product Owner(CSP-PO®), Certified Scrum Developer (CSD®), Certified Scrum Professional(CSP®), Certified Agile Leadership(CAL-I®,CAL-II®), Scrum Education Units(SEU®),Certified Scrum Trainer (CST®),Certified Enterprise Coach(CEC®), and Certified Team Coach(CTC®), are registered trademarks of Scrum Alliance®. SimpliAxis INC is a Licensed Training Partner (LTP) of Scrum Alliance.

Profession Scrum Master (PSM-I®, PSM-II®, PSM-III®), Profession Scrum Product Owner (PSPO-I®, PSPO-II®, PSPO-III®), Profession Scrum Developer (PSD-I®), Scaled Professional Scrum(SPS®),Professional Scrum With Kanban(PSK-I®) , Prove your knowledge of Professional Agile Leadership(PAL-I®), Prove your knowledge of Evidence-Based Management™ (PAL-EBM®), Prove Your Scrum with User Experience Knowledge(PSU-I®) and Professional Scrum Trainer(PST®) are registered trademarks of Scrum.org®. SimpliAxis INC is a Professional Training Network member of Scrum.org®.

Certified Business Analysis Professional (CBAP®), Certification of Capability in Business Analysis(CCBA®), Entry Certificate in Business Analysis(ECBA®), Agile Analysis Certification(AAC®), Certification in Business Data Analytics(CBDA®), Certificate in Cybersecurity Analysis(CCA®), Certificate in Product Ownership Analysis(CPOA®) are registered trademarks of International Institute of Business Analysis(IIBA®). SimpliAxis INC is an Premier Level Endorsed Education Provider of IIBA®.

SAFe Agilist Certification (SA®), SAFe Program Consultant Certification (SPC®),SAFe Program Consultant Trainer Certification (SPCT®),SAFe Practitioner Certification(SP®),SAFe Release Train Engineer Certification (RTE®),SAFe Scrum Master Certification (SSM®),SAFe Advanced Scrum Master Certification (SASM®),SAFe DevOps Practitioner Certification(SDP®),Agile Product Manager Certification (APM®),Lean Portfolio Manager Certification (LPM®),Product Owner / Product Manager Certification (POPM®),SAFe Architect Certification (ARCH®),Agile Software Engineer Certification (ASE®) and SAFe Government Practitioner Certification (SGP®), Scaled Agile Framework® and SAFe® are registered trademarks of Scaled Agile, Inc.®. SimpliAxis INC is a Platinum SPCT Partner of Scaled Agile, Inc®.

DevOps Foundation®, DevOps Leader®, SRE Foundation℠, SRE Practitioner℠, DevSecOps Foundation℠, Continuous Testing Foundation℠, Certified Agile Service Manager®, Continuous Delivery Ecosystem Foundation℠ and Value Stream Management Foundation® are registered trademarks of DevOps Institute.

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