Hands-On Agile Coaching | AI Forward Deployment Engineer (FDE) Master Program

Course Overview

The AI Forward Deployment Engineer (FDE) Master Program is a 12-week, hands-on Enterprise AI bootcamp built to take learners from FDE foundations all the way through a deployed Enterprise Capstone project. Across 48 days, participants master production Python, FastAPI backend engineering, Large Language Models, prompt engineering, embeddings and vector databases, and advanced Retrieval-Augmented Generation (RAG) systems.

The program then goes deep into AI Agents and multi-agent orchestration (ReAct, LangGraph, CrewAI, Google ADK), enterprise integrations (Slack, Teams, Jira, Google Workspace), Full-Stack AI with React and FastAPI, Docker/Cloud deployment and CI/CD, and LLM security, guardrails and observability. The final three weeks simulate real client engagements — requirement scoping, architecture design, Agile delivery — before learners design, build, and deploy their own Enterprise Capstone project.

Become the engineer who bridges AI research and production systems. Enroll today and build the end-to-end skills enterprises need to deploy AI that actually ships.

Course Objectives

This course aims to equip learners with the full stack of skills a Forward Deployment Engineer needs to take Enterprise AI from proof-of-concept to production. Participants will master production Python and FastAPI, LLM integration and prompt engineering, RAG and vector databases, autonomous AI agents, and the deployment, security, and client-delivery practices used on real enterprise engagements — using modern tools including LangGraph, CrewAI, Google ADK, Docker, and cloud platforms.

Understand the FDE role and the enterprise AI lifecycle from PoC to production.
Build production-grade async APIs with FastAPI, Pydantic, SQLAlchemy, and JWT-based security.
Master LLM fundamentals, prompt engineering, reasoning strategies, and stateful chatbot architecture.
Design embeddings-based retrieval and advanced RAG pipelines with re-ranking and hybrid search.
Build autonomous and multi-agent systems with the ReAct pattern, LangGraph, and CrewAI.
Integrate AI into enterprise ecosystems — Slack, Teams, Jira, and Google Workspace.
Ship full-stack AI products by connecting React frontends to streaming FastAPI backends.
Containerize and deploy AI applications to the cloud with Docker, CI/CD, and load-balanced infrastructure.
Apply LLM security, guardrails, red-teaming, and observability practices in production.
Run client-style scoping, architecture design, and Agile delivery on an Enterprise Capstone project.

Hurry up and join our AI Forward Deployment Engineer Master Program today to propel your career to greater heights.

12 Weeks Schedule: FDE Bootcamp Coverage

A comprehensive hands-on journey from FDE Foundations and FastAPI to LLMs, RAG, AI Agents, Full-Stack AI, Cloud Deployment, Security, and an Enterprise Capstone — Day 1 through Day 48.

12 weeks · 48 days · 196 topics · 146 hands-on labs

Day 1

Understand the core responsibilities of a Forward Deployment Engineer and how they bridge the gap between AI research models and scalable production systems. Learn the architectural principles required for enterprise deployment.

Topics Covered

Introduction to the Forward Deployment Engineer (FDE) role
The modern Enterprise AI lifecycle: From PoC to Production
System architecture for AI-native applications
Key challenges in enterprise AI: Latency, cost, security, and governance

Hands-On

Set up local development environment (VS Code, Python, Git)
Initialize repository with proper folder structure and environment configuration
Run baseline environment validation scripts

Day 2

Deep dive into setting up production-grade Python environments. Master dependency management using modern tooling, understand async/await syntax for high-concurrency applications, and write type-safe Python code.

Topics Covered

Advanced Python setup for production environments
Environment isolation: Virtualenv, Conda, and Poetry
Type hinting and asynchronous programming in Python
Best practices for code formatting, linting (Ruff/Black), and modular design

Hands-On

Set up Python environment management using Poetry/Pipenv
Configure auto-formatting, linting rules, and type checks
Build an asynchronous task processor in Python

Day 3

Learn enterprise version control strategies tailored for AI systems, including managing code changes alongside prompt iterations and data assets while maintaining strict governance.

Topics Covered

Git fundamentals and branching strategies for AI projects
Versioning datasets, prompt templates, and code
Pull request workflows and code review standards
CI/CD foundations with Git hooks

Hands-On

Set up GitHub repository with branch protection rules
Configure pre-commit hooks for code quality enforcement
Execute a mock multi-feature Git workflow with pull requests

Day 4

Master containerization concepts to ensure reproducible deployments across local development and cloud environments for Python and AI applications.

Topics Covered

Introduction to containerization with Docker
Writing efficient Dockerfiles for Python/AI workloads
Multi-stage Docker builds to reduce image sizes
Container networking and environment variable handling

Hands-On

Write Dockerfile for a Python application
Build multi-stage Docker container optimizing for size
Run and test containerized service locally

Day 5

Master building high-performance asynchronous web services using FastAPI. Learn RESTful design patterns needed to serve AI models at enterprise scale.

Topics Covered

Introduction to FastAPI and RESTful API architecture
Route handlers, path parameters, and query parameters
Async requests and high-concurrency handling
Automatic OpenAPI (Swagger) documentation generation

Hands-On

Build an asynchronous FastAPI application from scratch
Implement route handlers for dynamic queries
Verify API endpoints using OpenAPI Swagger interface

Day 6

Learn how to enforce strict data contracts between AI backend systems and external clients using Pydantic models to prevent runtime schema errors.

Topics Covered

Data validation using Pydantic schemas
Custom validation rules and field constraints
Serializing and deserializing complex JSON payloads
Error handling and HTTP status code standards

Hands-On

Create Pydantic data schemas for incoming client payloads
Implement custom validation logic for data structures
Implement robust global exception handlers in FastAPI

Day 7

Understand persistence layers in enterprise backend architecture. Implement ORM mapping and schema migrations to manage application data state safely.

Topics Covered

Connecting FastAPI to relational databases
Object-Relational Mapping (ORM) with SQLAlchemy / SQLModel
Database migrations using Alembic
Managing database sessions and connection pools

Hands-On

Configure SQLAlchemy database connections
Write and run database migrations using Alembic
Implement complete CRUD operations on PostgreSQL/SQLite

Day 8

Implement enterprise-grade security protocols to secure API endpoints, authenticate users using JWT tokens, and restrict access based on roles.

Topics Covered

Authentication & Authorization standards (OAuth2, JWT)
Hashing passwords and managing secrets securely
Role-Based Access Control (RBAC) in FastAPI
Completing the Student Management API project

Hands-On

Build JWT-based login and user authentication flows
Implement RBAC middleware for protected endpoints
Deploy and test the complete Student Management API

Day 9

Understand the core mechanics of Transformer-based Large Language Models. Learn how to interface with major model APIs programmatically while managing operational parameters and inference costs.

Topics Covered

Transformer architecture fundamentals: Self-attention and tokenization
API integration with proprietary models (OpenAI, Anthropic, Gemini)
Model parameters: Temperature, Top-P, Top-K, Presence/Frequency penalties
Calculating token usage, latency, and cost estimation

Hands-On

Integrate OpenAI and Anthropic API clients in Python
Experiment with generation parameters to analyze response variation
Build a token counter and cost estimation utility

Day 10

Learn how to design robust, deterministic prompt workflows that reliably return structured data suitable for integration into enterprise backend systems.

Topics Covered

Prompt Engineering principles: System prompts vs User prompts
Zero-shot, Few-shot, and In-context learning patterns
Structured output generation (JSON mode, Pydantic integration)
Prompt templates and dynamic variable injection

Hands-On

Design few-shot prompts for complex text classification
Enforce strict JSON output parsing using Pydantic schemas
Build a dynamic prompt templating engine

Day 11

Explore advanced prompting methodologies to solve multi-step reasoning problems and prevent prompt drift in production environments.

Topics Covered

Advanced reasoning strategies: Chain-of-Thought (CoT) and Tree-of-Thoughts (ToT)
Self-consistency and step-by-step reasoning validation
Handling long context windows and information loss ('lost in the middle')
System prompt hardening against jailbreaks

Hands-On

Implement Chain-of-Thought reasoning pipelines
Test self-consistency sampling to improve decision accuracy
Build prompt guard filters against basic adversarial prompts

Day 12

Learn how to maintain chat state across user interactions without exceeding model context limits or drastically increasing cost.

Topics Covered

Stateful conversational AI architecture
Conversation history management and memory truncation strategies
Context window compression and summarization techniques
Building an Enterprise AI Chatbot backend

Hands-On

Implement sliding-window and summary-based chat memory managers
Connect chatbot logic to FastAPI backend endpoints
Deploy functional Enterprise AI Chatbot core service

Day 13

Understand how unstructured text is converted into dense numerical vector representations for semantic search and retrieval systems.

Topics Covered

Introduction to Text Embeddings and high-dimensional vector spaces
Dense vs Sparse representations (TF-IDF vs Transformer Embeddings)
Distance metrics: Cosine Similarity, Euclidean Distance, Dot Product
Evaluating embedding model families (OpenAI, Hugging Face / BGE)

Hands-On

Generate text embeddings using Hugging Face sentence-transformers
Compute similarity scores between text document vectors
Build a basic in-memory semantic search engine

Day 14

Deep dive into vector database technologies used to store and index embedding vectors efficiently for real-time similarity search.

Topics Covered

Vector Database architecture: Indexing strategies (HNSW, IVF)
Introduction to ChromaDB, Pinecone, and Qdrant
Vector CRUD operations, metadata filtering, and collection management
Scaling vector search for high-throughput enterprise workloads

Hands-On

Set up and initialize ChromaDB vector store
Index collection of enterprise documents with metadata tags
Perform hybrid vector searches with metadata filtering

Day 15

Master the foundational RAG pipeline to allow LLMs to answer queries accurately based on private, domain-specific enterprise documents.

Topics Covered

Retrieval-Augmented Generation (RAG) architectural pattern
End-to-end RAG flow: Query -> Embedding -> Retrieval -> Context -> Generation
Context injection techniques and grounding model responses
Reducing hallucinations through context constraints

Hands-On

Build a baseline RAG pipeline in Python
Query vector database and construct prompt with retrieved context
Generate grounded answers and display source citations

Day 16

Understand data engineering pipelines required to process complex document types into clean, chunked text streams ready for embedding.

Topics Covered

Document ingestion pipelines for unstructured data
Parsing PDFs, DOCX, HTML, and Markdown files
Document cleaning, normalization, and deduplication
Text splitting techniques: Character, Recursive Character, Token-based

Hands-On

Parse complex PDF files with tables and text
Implement chunking strategies with overlap settings
Ingest processed chunks into a vector database

Day 17

Implement advanced retrieval optimization techniques to ensure maximum context quality and relevance for complex RAG systems.

Topics Covered

Advanced chunking: Semantic chunking, Propositional chunking, Hierarchical chunking
Parent-Child retriever pattern and Sentence Window retrieval
Hybrid Search: Combining Dense Vector Search with Sparse Keyword Search (BM25)
Reciprocal Rank Fusion (RRF) algorithms

Hands-On

Implement Semantic and Parent-Child chunking strategies
Combine Sparse (BM25) and Dense vector search results
Re-rank combined results using Reciprocal Rank Fusion

Day 18

Understand why organizations run models locally for privacy, cost reduction, and low latency. Install Ollama, download open-source models including Llama 3 and Mistral, and integrate them into your LangChain pipelines. Learn the…

Topics Covered

Why run models locally? Privacy, cost, and latency
Introduction to Ollama: downloading and running local LLMs
Local model families: Llama 3, Mistral, Phi-3, Gemma
Knowledge distillation: teacher-student model concept
Fine-tuning LLMs: full fine-tune vs LoRA vs QLoRA
Preparing a fine-tuning dataset
Fine-tuning with Hugging Face PEFT + TRL and Unsloth
Evaluating a fine-tuned model vs base model

Hands-On

Download and run Llama 3 locally with Ollama
Integrate local model with LangChain
Prepare a custom fine-tuning dataset
Fine-tune a small model with QLoRA using Unsloth
Compare base vs fine-tuned model responses

Day 19

Learn how to refine initial retrieval candidate sets using cross-encoder re-rankers and transform vague user queries to elevate search precision.

Topics Covered

Re-ranking models: Cross-Encoders vs Bi-Encoders
Implementing Cohere ReRank and Hugging Face BGE-Reranker
Query transformation techniques: Hypothetical Document Embeddings (HyDE)
Multi-query expansion and sub-query decomposition

Hands-On

Integrate Cross-Encoder re-ranker into retrieval pipeline
Implement HyDE query transformation pattern
Measure search accuracy improvements post-reranking

Day 20

Architect a production-ready RAG application complete with caching for reduced latency/cost, fallbacks for reliability, and automated evaluation metrics.

Topics Covered

Production RAG architecture: Caching, fallbacks, and error handling
Semantic caching of queries and responses (Redis / GPTCache)
Evaluating RAG performance (Faithfulness, Answer Relevance, Context Recall)
Deploying the Production RAG Knowledge Assistant

Hands-On

Implement Redis-based semantic cache for queries
Run automated evaluation metrics using RAGAS framework
Finalize and deploy the Production RAG endpoint

Day 21

Learn how to build autonomous agents capable of reasoning through complex tasks, dynamically selecting tools, and executing external code/APIs.

Topics Covered

Introduction to Agentic Architecture: Thought, Action, Observation (ReAct pattern)
Function Calling and Tool Definition schemas
Equipping LLMs with custom external tools (APIs, Calculators, Search)
Handling tool execution errors and recursive agent loops

Hands-On

Define custom Pydantic tool schemas for an LLM
Implement the ReAct reasoning loop from scratch
Test autonomous multi-tool execution flow

Day 22

Master LangGraph to build cyclical, stateful AI workflows capable of long-running executions, branching logic, and human approval steps.

Topics Covered

Stateful multi-actor workflows with LangGraph
State management, Graph nodes, Edges, and Conditional routing
Human-in-the-loop (HITL) approval workflows
Persisting graph state and time-travel debugging

Hands-On

Define a stateful workflow graph with custom state schemas
Implement conditional routing logic between graph nodes
Add Human-in-the-Loop breakpoint nodes to pause execution for review

Day 23

Understand how to divide complex organizational workflows into specialized autonomous AI agents operating collaboratively toward a shared objective.

Topics Covered

Multi-agent orchestration frameworks: CrewAI
Defining specialized Agent roles, goals, and backstories
Task delegation, sequential workflows, and hierarchical execution
Inter-agent communication and output passing

Hands-On

Configure specialized CrewAI agents (Researcher, Writer, Reviewer)
Set up task dependency graphs and execution pipelines
Run multi-agent team to generate comprehensive technical reports

Day 24

Explore Google ADK enterprise agent tools and build a robust, production-ready research agent that searches, synthesizes, and formats insights automatically.

Topics Covered

Google Agent Development Kit (ADK) integration
Enterprise multi-agent patterns and safety constraints
Benchmarking multi-agent performance and latency bottlenecks
Completing the Autonomous Research Agent Project

Hands-On

Configure Google ADK multi-agent pipeline
Implement safety guardrails on agent tool execution
Deliver complete Autonomous Research Agent project

Day 25

Master the integration mechanics needed to connect AI agents safely to third-party enterprise SaaS ecosystems via secure APIs and webhooks.

Topics Covered

Enterprise API integration patterns and OAuth 2.0 flows
Token management, refresh loops, and secure key storage
Webhook architectures for event-driven AI execution
Rate limiting, retries, and exponential backoff mechanisms

Hands-On

Build an OAuth2 token manager with automatic refresh handling
Construct FastAPI webhook listener for incoming events
Implement resilient API client with retry logic

Day 26

Learn how to automate document and email workflows within Google Workspace using structured AI generation pipelines.

Topics Covered

Google Workspace API integration (Gmail & Google Drive)
Parsing incoming emails, extracting attachments, and generating smart replies
Reading, creating, and updating Google Docs/Sheets programmatically
Document summarization and automated email drafting

Hands-On

Authenticate with Google Workspace API
Build email processing pipeline that reads and summarizes unread emails
Automatically generate and store email responses in Google Drive

Day 27

Understand how to bring AI assistants directly into employee chat interfaces (Slack/Teams) with rich interactive cards and fast response handling.

Topics Covered

Building interactive AI bots for Slack and Microsoft Teams
Event subscriptions, socket modes, and message payload parsing
Rendering rich UI components (Slack Block Kit, Teams Adaptive Cards)
Asynchronous processing for long-running AI queries in chat apps

Hands-On

Set up Slack App with Event Subscriptions
Render Slack Block Kit components for user input
Connect message handling logic to asynchronous AI pipeline

Day 28

Build an end-to-end enterprise solution that ingests meeting transcripts, extracts key decisions, generates Jira tickets, and posts summaries to Slack.

Topics Covered

Jira API integration for agile issue and workflow tracking
Automated ticket summarization, categorization, and priority assignment
Meeting transcript processing and action item extraction
Completing the AI Meeting Assistant Project

Hands-On

Process meeting transcript files using LLM extraction prompts
Create Jira tickets via Jira REST API automatically
Deploy completed AI Meeting Assistant integration service

Day 29

Learn how to rapidly prototype functional user interfaces for AI applications using pure Python to validate concepts with stakeholders quickly.

Topics Covered

Rapid UI prototyping for AI apps using Streamlit
Session state management, re-renders, and custom components
Displaying real-time streaming LLM responses in Streamlit
Building interactive dashboards for RAG and Chatbot testing

Hands-On

Build interactive Streamlit chat interface
Implement real-time token streaming using st.write_stream
Add sidebar controls for dynamic LLM hyperparameter tuning

Day 30

Master core frontend concepts with React to build custom production-grade client applications that consume enterprise AI APIs.

Topics Covered

React fundamentals for backend and FDE engineers
Component lifecycle, Hooks (useState, useEffect), and props
Building responsive web applications with Modern React & Tailwind CSS
State management patterns for complex user interfaces

Hands-On

Initialize React application project structure
Build custom reusable UI components using Tailwind CSS
Manage application state for chat messages and user options

Day 31

Implement full-stack communication protocols to stream tokens smoothly from FastAPI LLM services directly to React user interfaces.

Topics Covered

Connecting React frontend to FastAPI backend
Cross-Origin Resource Sharing (CORS) configuration
Server-Sent Events (SSE) and WebSockets for real-time streaming
Client-side error handling, loading spinners, and retry UI

Hands-On

Configure CORS middleware on FastAPI backend
Implement SSE endpoint in FastAPI for token streaming
Consume SSE stream in React frontend and update UI dynamically

Day 32

Assemble frontend and backend components into a cohesive, secure enterprise web portal capable of document upload, indexing, and chat query streaming.

Topics Covered

Designing production-ready Full-Stack AI user portals
User authentication flows between React and FastAPI
File upload handling for PDF document ingestion UI
Completing the Full-Stack AI Portal Project

Hands-On

Connect authentication headers to React API requests
Build drag-and-drop document upload component in React
Deliver fully integrated Full-Stack AI Portal application

Day 33

Learn how to orchestrate multi-service AI stacks locally and in production environments using Docker Compose configuration files.

Topics Covered

Advanced Docker container management for complex stacks
Docker Compose for multi-container orchestration (FastAPI, Redis, Postgres, Vector DB)
Container health checks, environment file management, and volume persistence
Optimizing container resource limits (CPU/GPU allocation)

Hands-On

Write docker-compose.yml for multi-service AI application
Configure service dependency networks and volume persistence
Spin up and validate full stack using docker compose up

Day 34

Understand cloud provider infrastructure components required to deploy, secure, and scale containerized AI applications reliably.

Topics Covered

Cloud infrastructure fundamentals for AI deployment (AWS / GCP)
Virtual Private Cloud (VPC), Security Groups, and IAM roles
Compute options: AWS EC2 / ECS / EKS vs GCP Cloud Run / GKE
Managed container registries (AWS ECR / GCP Artifact Registry)

Hands-On

Configure IAM roles and permissions for cloud deployment
Build and push Docker image to cloud container registry (ECR/Artifact Registry)
Provision cloud virtual server / container runtime instance

Day 35

Build automated pipelines that test code quality, build Docker containers, and deploy updates to cloud infrastructure seamlessly on every code commit.

Topics Covered

Continuous Integration and Continuous Deployment (CI/CD) pipelines
Automated testing and building with GitHub Actions
Secret management in CI/CD workflows
Automated deployment pipelines to cloud environments

Hands-On

Write GitHub Actions workflow file (.github/workflows/deploy.yml)
Configure automated unit testing execution on pull requests
Automate container image build and cloud service deployment

Day 36

Master web server configurations and load balancing strategies to serve production AI traffic securely with HTTPS and high uptime guarantees.

Topics Covered

Production web servers: Nginx, Traefik, and Gunicorn/Uvicorn
SSL/TLS certificate management (Let's Encrypt / Certbot)
Load balancing, auto-scaling groups, and health endpoints
Deploying AI Apps to the Cloud

Hands-On

Configure Nginx as reverse proxy with SSL termination
Set up domain DNS records and Certbot SSL certificates
Deploy and scale live AI application in cloud environment

Day 37

Understand security vulnerabilities unique to LLM systems and learn adversarial red-teaming methodologies to harden systems against attacks.

Topics Covered

OWASP Top 10 for Large Language Model applications
Prompt Injection attacks: Direct vs Indirect injection
Data leakage, PII exposure, and insecure output handling
Red teaming methodologies and adversarial vulnerability testing

Hands-On

Execute prompt injection attacks against unprotected endpoint
Build PII masking filter (regex + Presidio) for user input
Perform systematic red-teaming audit on AI endpoints

Day 38

Learn how to insert safety validation layers between user inputs, model generations, and system outputs to ensure policy compliance.

Topics Covered

Implementing enterprise safety guardrails (NeMo Guardrails, Llama Guard)
Programmable input, output, and topical rails
Hallucination detection and fact-checking filters
Enforcing brand guidelines and content moderation policies

Hands-On

Install and configure NeMo Guardrails / Llama Guard
Define topical rails to block off-topic queries
Add output verification filters for toxicity and hallucination checks

Day 39

Implement comprehensive observability dashboards to trace execution paths across multi-step chains, monitor API costs, and track latency metrics in production.

Topics Covered

LLM Observability and Tracing architectures
Integrating tracing tools: LangSmith, Phoenix (Arize), or LangFuse
Monitoring request latency, token consumption, and cost metrics
User feedback ingestion loops (Thumbs up/down tracking)

Hands-On

Instrument AI pipeline with LangSmith / LangFuse tracing
Capture step-by-step latency and token cost metrics
Implement user feedback logging endpoint in database

Day 40

Build an evaluation dashboard that systematically tracks model output accuracy, retrieval quality metrics, and cost spikes over time.

Topics Covered

Continuous evaluation of GenAI applications in production
Tracking model drift, retrieval decay, and performance degradation
Building custom evaluation pipelines using synthetic datasets
Completing the Evaluation Dashboard Project

Hands-On

Generate synthetic evaluation test datasets
Run automated daily quality benchmark scripts
Deploy live Evaluation Dashboard displaying key operational metrics

Day 41

Simulate real-world Forward Deployment Engineering scoping sessions. Learn how to convert ambiguous business requests into detailed technical project specs.

Topics Covered

Scoping enterprise AI projects: Deconstructing ambiguous client requests
Feasibility analysis: Deterministic rules vs AI/LLM solutions
Technical requirement gathering and constraint mapping
Drafting a Software Requirements Specification (SRS) for AI

Hands-On

Conduct simulated client requirement gathering session
Analyze trade-offs between heuristic rules and LLM models
Draft formal Software Requirements Specification (SRS)

Day 42

Learn how to design end-to-end enterprise architectures, choose appropriate technology components, and forecast running costs accurately.

Topics Covered

System Architecture Design for enterprise AI solutions
Selecting technology stacks: Model choice, Vector DBs, Cloud providers
Estimating operational infrastructure costs (Token math, GPU compute, DB storage)
Architectural diagramming standards (C4 Model)

Hands-On

Produce C4 component architecture diagrams for client solution
Build interactive monthly cost estimation model in Spreadsheet
Present system design proposal for technical peer review

Day 43

Understand how to apply Agile methodologies to non-deterministic AI engineering projects while maintaining strict delivery deadlines.

Topics Covered

Agile project management for GenAI development
Defining Minimum Viable Product (MVP) boundaries
Risk mitigation planning: Technical risks, data privacy, model performance
Sprint planning and milestone tracking for client deliverables

Hands-On

Break down system architecture into user stories and tasks
Create project backlog and sprint roadmap in Jira/GitHub Projects
Define acceptance criteria and Definition of Done (DoD)

Day 44

Master executive communication skills needed to present completed AI solutions, explain architectural trade-offs clearly, and run live client demos.

Topics Covered

Client delivery presentation strategies for technical leaders
Communicating complex AI trade-offs to non-technical executive stakeholders
Live demonstration best practices and fallback strategies
Completing the Client Delivery Exercise

Hands-On

Prepare executive-facing slide deck and architecture walkthrough
Conduct mock live client demonstration and Q&A defense
Collect feedback and finalize project handoff documentation

Day 45

Begin the comprehensive Capstone project. Define project scope, prepare real-world enterprise datasets, and setup core repository scaffolding.

Topics Covered

Enterprise Capstone Project kickoff and scope declaration
Selecting industry vertical (Finance, Healthcare, HR, Supply Chain)
Ingesting and preparing enterprise domain datasets
Finalizing Capstone repository and architecture plan

Hands-On

Submit Capstone proposal and architecture design
Setup repository scaffolding with Docker, CI/CD, and tests
Preprocess and ingest target domain dataset

Day 46

Execute core backend development for Capstone project, integrating advanced RAG or agentic mechanics with database persistence.

Topics Covered

Core backend logic and AI pipeline implementation
Implementing advanced RAG / Agentic workflows for Capstone
Connecting vector stores, relational databases, and API services
Optimizing query retrieval latency and token efficiency

Hands-On

Implement core FastAPI backend service endpoints
Connect vector search and LLM chain logic
Execute integration tests across core backend pipelines

Day 47

Connect frontend UI components to backend endpoints, apply security guardrails, and execute thorough end-to-end testing.

Topics Covered

Full-stack UI integration and frontend Polish
Implementing security guardrails, PII masking, and tracing
End-to-end user acceptance testing and bug fixes
Preparing cloud deployment environment

Hands-On

Wire React / Streamlit frontend to FastAPI backend service
Apply guardrails, PII filters, and observability instrumentation
Execute full end-to-end integration test suite

Day 48

Deploy the complete Capstone project to live cloud infrastructure, present the solution to technical evaluators, and complete the bootcamp program.

Topics Covered

Production cloud deployment of Capstone solution
Final performance validation and live endpoint health checks
Capstone presentation, defense, and code audit
Program completion and Enterprise AI FDE Certification

Hands-On

Deploy Capstone application to live cloud URL with SSL
Present live project demonstration and architecture defense
Receive code review feedback and program completion certification

Projects Covered in the 12-Week Program

Async Task Processor & Dockerized Service: Production-grade Python setup, Git workflows, and a containerized service — the FDE foundations project.
Student Management API: A complete FastAPI service with SQLAlchemy/Alembic persistence, JWT authentication, and Role-Based Access Control.
Enterprise AI Chatbot: A stateful conversational backend with sliding-window memory and structured, deterministic prompt workflows.
Production RAG Knowledge Assistant: A cached, evaluated RAG pipeline covering ingestion, chunking, retrieval, and grounded generation.
Autonomous Research Agent: A ReAct-based agent with custom tools, extended into a stateful LangGraph workflow and a CrewAI multi-agent team.
AI Meeting Assistant: An enterprise integration project that extracts action items from transcripts and files Jira tickets automatically.
Full-Stack AI Portal: A React + FastAPI application with authentication, document upload, and real-time SSE token streaming.
Live Cloud Deployment: A Dockerized, CI/CD-automated deployment behind Nginx with SSL, load balancing, and health checks.
Evaluation & Guardrails Dashboard: A production observability dashboard with PII filters, red-teamed guardrails, and RAGAS evaluation metrics.
Enterprise Capstone Project: An end-to-end AI solution — scoped, architected, built, secured, and deployed to a live cloud URL with a client-style defense.

Bonuses

FDE & Enterprise AI interview questions & answers
Real-time client scoping scenarios and solutions
Cloud (AWS/GCP) AI deployment certification support
Resume/CV preparation
Building a Capstone-backed project portfolio
LinkedIn profile optimization
Placement Assistance
On-Job Support

Target Jobs

AI Forward Deployment Engineer
Enterprise AI Engineer/Consultant
AI Solutions Architect
LLM/GenAI Engineer
AI Agent Developer
Full-Stack AI Engineer

Program Outcome

Transition your career into Forward Deployment Engineering and Enterprise AI roles.
Land high-paying AI deployment and solutions engineering jobs globally.
Ship a deployed, defensible Enterprise Capstone project for your portfolio.
Switch to Top IT & Product based companies.
Have a secured career and work in a Future trend job.
Become a top & highly paid IT professional.

FAQ

A: It is a 12-week, 48-day hands-on Enterprise AI bootcamp that takes learners from FDE foundations and FastAPI backend engineering through LLMs, RAG, AI Agents, Full-Stack AI, cloud deployment, and security to a deployed Enterprise Capstone project.

A: This program is ideal for developers, backend engineers, data scientists, and AI enthusiasts who want to become production-ready Enterprise AI engineers capable of deploying AI systems that meet real business requirements.

A: Basic Python programming knowledge is recommended. The program is structured to build learners up from Python and FastAPI foundations to advanced LLM, RAG, and multi-agent engineering step by step.

A: Unlike theory-heavy programs, this is a Forward Deployment Engineer track — every week ends in a hands-on lab or project, and the final three weeks simulate real client requirement scoping, architecture design, and delivery before a live Capstone deployment.

A: You'll work with FastAPI, Pydantic, SQLAlchemy, Docker, LangChain, LangGraph, CrewAI, Google ADK, ChromaDB/Pinecone/Qdrant, React, GitHub Actions, Nginx, NeMo Guardrails, and cloud platforms including AWS and GCP.

A: By the end, participants will be able to design, build, secure, and deploy production Enterprise AI systems — from RAG pipelines and multi-agent workflows to full-stack AI portals — and defend an architecture in a client-style review.

A: Learners build a Student Management API, an Enterprise AI Chatbot, a Production RAG Knowledge Assistant, an Autonomous Research Agent, an AI Meeting Assistant, a Full-Stack AI Portal, and a deployed Enterprise Capstone project.

A: The program is divided into 12 weeks and 48 days, progressing from FDE foundations and FastAPI, through LLMs, RAG, AI Agents, Enterprise Integrations, Full-Stack AI, Cloud Deployment & CI/CD, Security & Guardrails, Client Simulation, and the Enterprise Capstone.

A: Yes. Participants who complete all 12 weeks and the Enterprise Capstone project successfully receive an Enterprise AI FDE Certification validating their end-to-end deployment expertise.

A: Graduates gain in-demand skills to pursue roles such as AI Forward Deployment Engineer, Enterprise AI Engineer, AI Solutions Architect, LLM/GenAI Engineer, and Full-Stack AI Engineer, and are equipped to lead real enterprise AI deployments.

Instructor

Nitesh

Forward Deployment Engineer Trainer & Enterprise AI Consultant · London, UK

1227 Reviews 4.4 Rating
910 Students
12 Courses

Nitesh is based in London and brings strong hands-on expertise in Enterprise AI deployment, backend engineering, and Forward Deployment Engineering practice. He has trained many students globally on production FastAPI, RAG systems, AI agents, and end-to-end enterprise AI delivery. He has also successfully trained students from UK universities and learners across 30+ countries.

His core expertise includes production Python and FastAPI, LLM and RAG engineering, multi-agent systems with LangGraph and CrewAI, cloud deployment and CI/CD, and client-facing scoping and delivery for real enterprise AI projects.

With a practical, implementation-focused, and industry-oriented teaching approach, Nitesh is passionate about helping students build real-world Forward Deployment Engineering skills aligned with current global market demand.