FORWARD DEPLOYED AI ENGINEERING PROGRAM

Become a Forward DeployedAI Engineer

Build production AI systems and take real business problems from discovery to deployment.

Download Curriculum
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THE OPPORTUNITY

Why Are Companies Betting Big on FDEs?

Understand the Business

Understand the Business

Solve the right problem.

Build the Solution

Build the Solution

Turn it into a working AI system.

Deliver Real-World Impact

Deliver Real-World Impact

Deploy it where it matters.

Business Problem Icon

Business
Problem

Business Problem Icon

FDE

BRIDGES IDEAS
TO IMPACT

Real-World Impact Icon

Real-World
Impact

WHO IS THIS PROGRAM FOR?

Who Can Become anFDE?

Your existing IT skills may already be relevant to an FDE role.

Software Developers

Use your coding and development experience to build and deploy AI solutions in real-world scenarios.

Your existing skills FDE

Data Professionals

Use your data and SQL experience to work with business data and build practical AI solutions.

Your existing skills FDE

QA & Automation Engineers

Apply your testing and automation experience to improve the reliability and performance of AI systems.

Your existing skills FDE

Cloud, DevOps & IT Operations

Use your cloud, infrastructure or operations experience to deploy and manage AI solutions in production environments.

Your existing skills FDE

Ready to Plan Your Move to FDE?

Whatever your IT background, let's explore your career transition options.

PREPZEE ALUMNI

They Wrote Their Next Chapter. You Can Too

Explore the career journeys of our Data Engineering and AI alumni.

Kishan Yadav

Kishan Yadav

Data Engineer at Microsoft

Microsoft Microsoft
in
BEFORE Data Analytics
AFTER Data Engineer @ Microsoft
View LinkedIn Profile
Mridul Jangid

Mridul Jangid

AI Engineer

Walmart Walmart
in
BEFORE Software Engineer
AFTER AI Engineer @ Walmart
View LinkedIn Profile
Mayank Sharma

Mayank Sharma

Data Engineer

Databricks Databricks
in
BEFORE Data Analyst
AFTER Data Engineer
@ Databricks
View LinkedIn Profile
PROGRAM CURRICULUM

Explore the FDE Curriculum

Download Detailed Curriculum
MODULE 01 / 09

Python & API Development

TOPICS COVERED

01Python Fundamentals

  • Python syntax, variables & data types
  • Operators, conditions & control flow
  • Lists, tuples, sets & dictionaries
  • Functions & modular programming
  • File & JSON handling

02Python for AI Applications

  • HTTP requests & REST APIs
  • Virtual environments & dependencies
  • LLM API integration
  • Async programming basics
  • Error & exception handling

03 FastAPI & Production APIs

  • Building REST APIs with FastAPI
  • Request & response models
  • Data validation with Pydantic
  • Background tasks & middleware
  • Logging & debugging
  • Authentication & secure configuration
MODULE 02 / 09

Vibe Coding & AI-Assisted Development

TOPICS COVERED

01 AI-Assisted & Spec-First Development

  • Claude Code / Cursor
  • Project instructions & context
  • AI-assisted code generation
  • Extending existing applications
  • Defining requirements before coding
  • Breaking requirements into tasks
  • Implementation planning
  • Step-by-step AI development

02Code Review, Testing & Security

  • Reviewing AI-generated Git diffs
  • Detecting hallucinated APIs
  • Identifying logic & security issues
  • Debugging & refining generated code
  • Unit & API testing
  • Regression testing
  • Edge-case & failure testing
  • Code & dependency security checks
MODULE 03 / 09

LLMs & Prompt Engineering

TOPICS COVERED

01LLM Foundations & Transformers

  • How Large Language Models work
  • Tokenization & context windows
  • Next-token prediction
  • Pre-trained models vs fine-tuning
  • Transformer architecture
  • Attention mechanism

02 Prompt Engineering

  • Zero-shot & few-shot prompting
  • Chain-of-thought techniques
  • System & role-based prompts
  • Prompt debugging & optimization
  • Working with Hugging Face
  • Models & pipelines

03 LLM APIs & Applications

  • OpenAI, Claude & Gemini APIs
  • Function calling & JSON outputs
  • Conversation flow & context
  • Building AI assistants
MODULE 04 / 09

RAG & Vector Intelligence

TOPICS COVERED

01 Embeddings & Vector Search

  • Generating text embeddings
  • Vector representation of text
  • Cosine similarity & matching
  • Building semantic search
  • FAISS & ChromaDB
  • Embedding indexing

02 Vector Databases & Core RAG

  • Fast vector retrieval
  • Persistence & updates
  • RAG pipeline architecture
  • Data ingestion & chunking
  • Context injection into LLMs
  • Multi-source document retrieval

03 Advanced RAG

  • Multi-query & hybrid retrieval
  • Reranking techniques
  • Context optimization
  • Reducing hallucinations
MODULE 05 / 09

AI Agents

TOPICS COVERED

01LangChain & Agent Fundamentals

  • Chains & Retrievers
  • LLM Integrations
  • Connecting Databases & APIs
  • Reusable AI Pipelines
  • AI Agents & Workflows
  • Planning & Reasoning
  • Autonomous Decision-Making
  • Intelligent Assistants

02 Tools, Memory & Agent Workflows

  • Tool Usage by AI Agents
  • External API Integration
  • Connecting Agents with Systems
  • Multi-Step Task Execution
  • Short-Term Memory
  • Long-Term Memory
  • Context Management
  • Agent Workflow Orchestration
MODULE 06 / 09

Multi-Agent Systems

TOPICS COVERED

01LangGraph & Stateful Workflows

  • LangGraph Fundamentals
  • Graph-Based Agent Workflows
  • Nodes, Edges & Execution Flow
  • Building Agent Workflows
  • State-Based Execution
  • Managing Agent State
  • Complex Workflow Management
  • Controlled Agent Execution

02 Multi-Agent Systems

  • Designing Multi-Agent Systems
  • Task Delegation & Coordination
  • Agent Communication Patterns
  • Collaborative AI Workflows

03 Model Context Protocol (MCP)

  • MCP Architecture
  • Connecting Models & Tools
  • Context Across Components
  • Modular AI Integrations
MODULE 07 / 09

AI System Design

PROJECT JOURNEY

01AI & Enterprise Architecture

  • End-to-End AI System Architecture
  • LLM, RAG & Agent Components
  • APIs, Databases & Data Flows
  • Model Selection & Routing
  • Enterprise System Integration
  • Cloud vs On-Premise Considerations
  • Security & Data Privacy
  • Scalability, Latency & Cost

02 High-Level & Low-Level Design

  • Components & Service Interactions
  • Architecture & Data-Flow Diagrams
  • Non-Functional Requirements
  • Technology Trade-Offs
  • API Contracts & Schemas
  • Component-Level Design
  • Sequence & Failure-Flow Diagrams
  • Fallback Strategies & ADRs
MODULE 08 / 09

LLMOps & Deployment

PROJECT JOURNEY

01LLM Evaluation & Guardrails

  • LLM Evaluation Techniques
  • Response Quality Assessment
  • Hallucination Detection
  • Prompt & Model Regression Testing
  • Output Validation & Guardrails

02 Observability & Production Deployment

  • LangSmith Tracing & Debugging
  • Logging LLM Interactions
  • Latency, Token & Cost Monitoring
  • Production APIs with FastAPI
  • Docker & Docker Compose
  • GitHub Actions CI/CD

03Cloud Deployment & Scaling

  • Release & Deployment Workflows
  • Deploying AI Applications on AWS
  • EC2 & Container Deployment
  • Caching, Batching & Failure Handling
  • Scaling & Cost Optimization
MODULE 09 / 09

Forward Deployed AI Engineering

PROJECT JOURNEY

01Discovery & Solution Scoping

  • Customer Discovery & Requirements
  • Identifying the Real Problem & AI Fit
  • AS-IS -> TO-BE Workflow Mapping
  • Scope, Non-Goals & Success Metrics
  • Enterprise APIs & Data Sources

02Enterprise Integration & Client Delivery

  • Authentication, SSO & OAuth
  • Data Privacy, PII & Security
  • Legacy & Undocumented Systems
  • BRD, TDD & Implementation Planning
  • UAT & Acceptance Criteria
  • Go-Live, Rollback & Incident Handling
  • Production Handover & Runbooks

03Stakeholder Management & Forward Deployment

  • Communicating Technical Trade-Offs
  • Architecture & Solution Walkthroughs
  • User Training & Adoption
  • Success Metrics & Impact Reporting
MODULE 01 / 09

Python & API Development

01. Python Fundamentals

  • Python syntax, variables & data types
  • Operators, conditions & control flow
  • Lists, tuples, sets & dictionaries
  • Functions & modular programming
  • File & JSON handling

02. Python for AI Applications

  • HTTP Requests & REST APIs
  • Virtual Environments & Dependencies
  • LLM API Integration
  • Async Programming Basics
  • Error & Exception Handling

03. FastAPI & Production APIs

  • Building REST APIs with FastAPI
  • Request & Response Models
  • Data Validation with Pydantic
  • Background Tasks & Middleware
  • Logging & Debugging
  • Authentication & Secure Configuration
MODULE 02 / 09

Vibe Coding & AI-Assisted Development

TOPICS COVERED

01 AI-Assisted & Spec-First Development

  • Claude Code / Cursor
  • Project instructions & context
  • AI-assisted code generation
  • Extending existing applications
  • Defining requirements before coding
  • Breaking requirements into tasks
  • Implementation planning
  • Step-by-step AI development

02Code Review, Testing & Security

  • Reviewing AI-generated Git diffs
  • Detecting hallucinated APIs
  • Identifying logic & security issues
  • Debugging & refining generated code
  • Unit & API testing
  • Regression testing
  • Edge-case & failure testing
  • Code & dependency security checks
MODULE 03 / 09

LLMs & Prompt Engineering

TOPICS COVERED

01LLM Foundations & Transformers

  • How Large Language Models work
  • Tokenization & context windows
  • Next-token prediction
  • Pre-trained models vs fine-tuning
  • Transformer architecture
  • Attention mechanism

02 Prompt Engineering

  • Zero-shot & few-shot prompting
  • Chain-of-thought techniques
  • System & role-based prompts
  • Prompt debugging & optimization
  • Working with Hugging Face
  • Models & pipelines

03 LLM APIs & Applications

  • OpenAI, Claude & Gemini APIs
  • Function calling & JSON outputs
  • Conversation flow & context
  • Building AI assistants
MODULE 04 / 09

RAG & Vector Intelligence

TOPICS COVERED

01 Embeddings & Vector Search

  • Generating text embeddings
  • Vector representation of text
  • Cosine similarity & matching
  • Building semantic search
  • FAISS & ChromaDB
  • Embedding indexing

02 Vector Databases & Core RAG

  • Fast vector retrieval
  • Persistence & updates
  • RAG pipeline architecture
  • Data ingestion & chunking
  • Context injection into LLMs
  • Multi-source document retrieval

03 Advanced RAG

  • Multi-query & hybrid retrieval
  • Reranking techniques
  • Context optimization
  • Reducing hallucinations
MODULE 05 / 09

AI Agents

TOPICS COVERED

01LangChain & Agent Fundamentals

  • Chains & Retrievers
  • LLM Integrations
  • Connecting Databases & APIs
  • Reusable AI Pipelines
  • AI Agents & Workflows
  • Planning & Reasoning
  • Autonomous Decision-Making
  • Intelligent Assistants

02 Tools, Memory & Agent Workflows

  • Tool Usage by AI Agents
  • External API Integration
  • Connecting Agents with Systems
  • Multi-Step Task Execution
  • Short-Term Memory
  • Long-Term Memory
  • Context Management
  • Agent Workflow Orchestration
MODULE 06 / 09

Multi-Agent Systems

TOPICS COVERED

01LangGraph & Stateful Workflows

  • LangGraph Fundamentals
  • Graph-Based Agent Workflows
  • Nodes, Edges & Execution Flow
  • Building Agent Workflows
  • State-Based Execution
  • Managing Agent State
  • Complex Workflow Management
  • Controlled Agent Execution

02 Multi-Agent Systems

  • Designing Multi-Agent Systems
  • Task Delegation & Coordination
  • Agent Communication Patterns
  • Collaborative AI Workflows

03 Model Context Protocol (MCP)

  • MCP Architecture
  • Connecting Models & Tools
  • Context Across Components
  • Modular AI Integrations
MODULE 07 / 09

AI System Design

PROJECT JOURNEY

01AI & Enterprise Architecture

  • End-to-End AI System Architecture
  • LLM, RAG & Agent Components
  • APIs, Databases & Data Flows
  • Model Selection & Routing
  • Enterprise System Integration
  • Cloud vs On-Premise Considerations
  • Security & Data Privacy
  • Scalability, Latency & Cost

02 High-Level & Low-Level Design

  • Components & Service Interactions
  • Architecture & Data-Flow Diagrams
  • Non-Functional Requirements
  • Technology Trade-Offs
  • API Contracts & Schemas
  • Component-Level Design
  • Sequence & Failure-Flow Diagrams
  • Fallback Strategies & ADRs
MODULE 08 / 09

LLMOps & Deployment

PROJECT JOURNEY

01LLM Evaluation & Guardrails

  • LLM Evaluation Techniques
  • Response Quality Assessment
  • Hallucination Detection
  • Prompt & Model Regression Testing
  • Output Validation & Guardrails

02Observability & Production Deployment

  • LangSmith Tracing & Debugging
  • Logging LLM Interactions
  • Latency, Token & Cost Monitoring
  • Production APIs with FastAPI
  • Docker & Docker Compose
  • GitHub Actions CI/CD

03Cloud Deployment & Scaling

  • Release & Deployment Workflows
  • Deploying AI Applications on AWS
  • EC2 & Container Deployment
  • Caching, Batching & Failure Handling
  • Scaling & Cost Optimization
MODULE 09 / 09

Forward Deployed AI Engineering

PROJECT JOURNEY

01Discovery & Solution Scoping

  • Customer Discovery & Requirements
  • Identifying the Real Problem & AI Fit
  • AS-IS -> TO-BE Workflow Mapping
  • Scope, Non-Goals & Success Metrics
  • Enterprise APIs & Data Sources

02Enterprise Integration & Client Delivery

  • Authentication, SSO & OAuth
  • Data Privacy, PII & Security
  • Legacy & Undocumented Systems
  • BRD, TDD & Implementation Planning
  • UAT & Acceptance Criteria
  • Go-Live, Rollback & Incident Handling
  • Production Handover & Runbooks

03Stakeholder Management & Forward Deployment

  • Communicating Technical Trade-Offs
  • Architecture & Solution Walkthroughs
  • User Training & Adoption
  • Success Metrics & Impact Reporting
HANDS-ON PROJECTS

Build Real-World Projects for Your FDE Portfolio.

PROJECT 01 / 05

Production-Grade Multi-Agent AI Assistant

AI ENGINEERING

Build an intelligent AI assistant that researches information, coordinates multiple specialized agents, and completes complex tasks autonomously.

What You’ll Build

⚙

AI orchestrator
that routes tasks

⌕

RAG, web-search
and code agents

▤

Shared memory
and observability

▣

A working assistant
with an interactive UI

System Architecture

Architecture Diagram
Tech Stack
Python Python FastAPI FastAPI Redis Redis ChromaDB ChromaDB E2B E2B Docker Docker
PROJECT 02 / 05

OpsPilot — AI Incident Response & Resolution Agent

AI ENGINEERING

Build an agentic AI system that investigates incidents, coordinates resolution workflows, and interacts with enterprise tools through MCP.

What You'll Build

⚙

AI orchestrator
that routes tasks

⌕

RAG, web-search
and code agents

▤

Shared memory
and observability

▣

A working assistant
with an interactive UI

System Architecture

Architecture Diagram
PROJECT 03 / 05

ScamGuard AI — AI-Powered Scam Detection & Risk Analysis

AI ENGINEERING

Build an AI-powered system that detects scams, identifies manipulative intent, and explains the risk in real time.

What You’ll Build

⚙

AI orchestrator
that routes tasks

⌕

RAG, web-search
and code agents

▤

Shared memory
and observability

▣

A working assistant
with an interactive UI

System Architecture

Architecture Diagram
Tech Stack
Python Python FastAPI FastAPI Redis Redis ChromaDB ChromaDB E2B E2B Docker Docker
PROJECT 04 / 05

SupportIQ — AI Customer Support Response Optimizer

AI ENGINEERING

Build an AI-powered support system that understands customer queries and generates accurate, structured, and context-aware responses.

What You’ll Build

⚙

AI orchestrator
that routes tasks

⌕

RAG, web-search
and code agents

▤

Shared memory
and observability

▣

A working assistant
with an interactive UI

System Architecture

Architecture Diagram
Tech Stack
Python Python FastAPI FastAPI Redis Redis ChromaDB ChromaDB E2B E2B Docker Docker
PROJECT 05 / 05

HireFlow — Intelligent Candidate Search & Evaluation

AI ENGINEERING

Build an AI-powered hiring system that semantically matches candidates to job requirements and explains why they're a strong fit.

What You’ll Build

⚙

AI orchestrator
that routes tasks

⌕

RAG, web-search
and code agents

▤

Shared memory
and observability

▣

A working assistant
with an interactive UI

System Architecture

Architecture Diagram
Tech Stack
Python Python FastAPI FastAPI Redis Redis ChromaDB ChromaDB E2B E2B Docker Docker
PROGRAM OVERVIEW

Program Details at a Glance

Next Batch

Next Batch

18 October 2026

Sat – Sun · 7 PM to 10 PM IST

Live Training

Live Training

100+ Hours

16-week instructor-led program.

No-Cost EMI

No-Cost EMI

From ₹3,500/month

Pay comfortably with no-cost
EMI options.

Class Recordings

Class Recordings

Anytime Revision

Revisit completed sessions
anytime.

Technical Support

Technical Support

24×7 Assistance

Get help with concepts and
project challenges.

Live Training Access

Live Training Access

Lifetime Access

Rejoin future live batches to
continue learning.

Call Us Chat on WhatsApp
LEARNER REVIEWS

That's what They Said

Real learners. Real learning experiences.

“
I completed the Generative AI Program at Prepzee, gaining hands-on experience in Python, Prompt Engineering, LLMs, and AI model deployment. The program’s practical approach and great support from the Prepzee team helped me apply AI concepts effectively and advance my career in Generative AI.
Harsh Raj
Harsh Raj●

GenAI Developer @Tata Consultancy Services

Generative AI Alumni
in
Manisha Kumari review
My Data & AI Engineer
Journey with Prepzee
07:26
Jenny
Kevin ●

Data & AI Engineer @ P&G

Data & AI Engineer Alumni
in
“
GenAI sessions were highly interactive and well-structured kuddos to instructor , GenAI and Machine Learning concepts, including model building, data preprocessing, and transformers — all explained with real-world examples. Hands-on practice made learning both engaging and effective. If you’re looking to strengthen your understanding of data analysis, statistics, Python programming, and GenAI/ML, I highly recommend taking his class. It’s perfect for beginners as well as professionals who want to build a strong analytical and AI Knowledge
Suman Choudhary
Mohshin Sarker ●

Senior Financial Analyst @ McKesson Canada

GenAI Alumni
in
“
What makes this course stand out is how real and practical it feels. We weren't just learning theory — we were actually working on AI projects that felt relevant to today's world. The GenAI section especially was eye-opening. This is the kind of course that actually prepares you for the industry.
Rohan Mulay
Rohan Mulay ●

Chief Manager @ L&T Finance

GenAI Alumni
in
“
The AI/ML course at Prepzee was an excellent learning experience that combined strong practical application. The clear and engaging teaching approach made complex concepts easy to understand, while hands-on projects and real-world examples strengthened my skills. With a great balance of lessons, interaction, and support, Prepzee proved to be an ideal platform for mastering AI and Machine Learning.
Narayan
Narayan Mishra●

Information Technology Associate @ Amazon

AI/ML Alumni
in
Manisha Kumari review
My Data Engineering
Journey with Prepzee
00:36
Manisha'
Manisha●

Data Engineer @ HCL

Data Engineering Alumni
in
“
The GenAI & ML Program has been an amazing learning experience! The ‘learn while you watch the live sessions’ concept really helps me understand each topic step by step. The roadmap, lab project lists, and access to recordings are all provided in advance, which makes it easy to follow along and stay prepared. The faculty are highly skilled and make every session feel like a real hands-on experience. Truly a great way to learn and grow!
Viveka Ramchandran
Muhammad Danish ●

Software Prod & Plat Eng Asst. Manager @ Accenture

GenAI & ML Alumni
in
Ranjith review
My Data Engineering
Journey with Prepzee
02:14
Ranjith
Ranjith Kumar ●

Developer @ Cisco

Data Science Alumni
in
“
GenAI sessions were highly interactive and well-structured kuddos to instructor , GenAI and Machine Learning concepts, including model building, data preprocessing, and transformers — all explained with real-world examples. Hands-on practice made learning both engaging and effective. If you’re looking to strengthen your understanding of data analysis, statistics, Python programming, and GenAI/ML, I highly recommend taking his class. It’s perfect for beginners as well as professionals who want to build a strong analytical and AI Knowledge
Kevin Keller Kenga
Shurti Tawde ●

HR @ JM Financial Services Ltd

GenAI Alumni
in
YOUR CAREER JOURNEY

Your 4-Step Journey to Becoming a
Forward Deployed AI Engineer

♧
STEP 01

Learn the Foundations

Build AI engineering skills through live, instructor-led sessions.

<>
STEP 02

Build Industry-Level Projects

Apply what you learn to real-world AI projects and build your FDE portfolio.

▣
STEP 03

Get Career-Ready with Expert Guidance

Get expert-led interview preparation, resume-building support and guidance on relevant FDE job opportunities.

▢
STEP 04

Land Your FDE Role

Showcase your portfolio and interview skills as you pursue your next career opportunity.

PROGRAM ENROLLMENT

Program Fee & Payment Options

📅
UPCOMING COHORT
October 18, 2026 Enrollment Open
◷
LIVE SESSIONS Saturday & Sunday • 9:30 AM ET
▱
PROGRAM DURATION Oct 18, 2026 → Jan 31, 2027

16 Weeks of structured, hands-on learning.

Program Fee

₹120,000.00 SAVE ₹20,000.00
₹100,000.00
Enrollment Price
▰
NO-COST EMI AVAILABLE Choose a convenient payment plan.
Enroll Now →
🔒 Secure Checkout ◷ 14-Day Refund Policy

Have questions before enrolling?

☎ Call a Program Advisor →
FAQ

Frequently Asked Questions

A Forward Deployed AI Engineer works closely with real business problems and helps design, build, deploy and improve production AI systems.
The program is suitable for software developers, data professionals, QA engineers and cloud or DevOps professionals looking to transition into AI engineering roles.
The program includes instructor-led live sessions along with project work and technical support.
Yes. The program includes multiple production-style AI projects designed to help you build a practical FDE portfolio.

Ready to Start Your FDE Journey?

Speak with a Prepzee program advisor and explore whether this program is right for you.

PERSONALIZED CAREER GUIDANCE

Let’s Plan
Your Move to FDE.

Your Experience
Your FDE Career Path

    STEP 1 OF 2

    Let’s Connect!


    A Prepzee advisor will contact you to discuss
    your FDE career path.


    Your details are safe with us. We’ll use them only
    to contact you about your FDE career guidance.

    STEP 2 OF 2


    Tell Us About Your Background

    Enroll Now → Chat Now
    ☎

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