Forward Deployed Engineer (FDE) Master Program: GenAI, RAG, Agents & Enterprise AI

Forward Deployed Engineer (FDE) Master Program: GenAI, RAG, Agents & Enterprise AI – Live Training

(Generative AI, LLMs, Prompt Engineering, RAG, Vector Databases, AI Agents, Multi-Agent Systems, MCP, LangChain, LangGraph, AI Automation, Data Engineering, Cloud Architecture, AI-Assisted Development & Enterprise AI Delivery.)

The Forward Deployed Engineer (FDE) Master Program is a comprehensive, hands-on training designed to equip learners with the skills required to build, deploy, and deliver Enterprise AI solutions. This program covers the complete AI engineering lifecycle, including Generative AI (GenAI), Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), Embeddings, Vector Databases, AI Agents, Multi-Agent Systems, MCP (Model Context Protocol), LangChain, LangGraph, and AI Workflow Automation. Participants will learn how to select, customize, and optimize AI models, build intelligent applications, create advanced RAG pipelines, and develop agentic AI systems that solve real-world business challenges.

In addition to AI engineering, the course provides practical knowledge in Data Engineering, Cloud Architecture, AI-Assisted Software Development, AWS Services, Docker, CI/CD, Data Pipelines, ETL/ELT, Data Lakes, Lakehouses, and Enterprise AI Delivery Frameworks. Through real-time projects, labs, case studies, and industry scenarios, learners will gain expertise in translating business requirements into scalable AI solutions, building production-ready applications, automating workflows, and delivering measurable business value. By the end of the program, participants will be ready for roles such as Forward Deployed Engineer (FDE), AI Engineer, AI Solutions Architect, GenAI Engineer, Agentic AI Developer, AI Consultant, and Enterprise AI Specialist.

About the Instructor:

Tim Chowdary – Forward Deployed Engineering (FDE), Enterprise AI & Generative AI Expert

Tim Chowdary is a highly experienced technology professional with 19+ years of expertise in Enterprise AI, Forward Deployed Engineering (FDE), Data Engineering, Cloud Technologies, Digital Transformation, and Enterprise Solution Architecture. He specializes in helping organizations transform business challenges into scalable AI-powered solutions through a combination of technical expertise, consulting, and hands-on implementation.

With deep expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Multi-Agent Systems, AI Automation, Data Engineering, Cloud Architecture, and Enterprise AI Delivery, Tim has successfully designed and delivered numerous enterprise-scale solutions across diverse industries.

As a Forward Deployed Engineer (FDE), Tim bridges the gap between business stakeholders and technical teams. His experience includes client discovery workshops, requirements gathering, solution architecture, rapid prototyping, proof-of-concept development, AI application deployment, and enterprise-scale implementation. He helps organizations identify high-value AI opportunities and transform them into practical, production-ready solutions.

Tim is also highly skilled in modern AI engineering practices, including Prompt Engineering, AI Agents, MCP (Model Context Protocol), AI Workflows, AI-Assisted Software Engineering, Enterprise RAG Systems, Cloud-Native AI Applications, and Data Platform Modernization. His practical approach enables organizations to accelerate AI adoption while maintaining scalability, security, and business alignment.

A passionate trainer and mentor, Tim has delivered 150+ training programs, workshops, and technical sessions, helping hundreds of professionals upskill in Forward Deployed Engineering (FDE), Enterprise AI, Data Engineering, Cloud Computing, and AI Solution Delivery. His training methodology focuses on real-world consulting scenarios, hands-on labs, industry use cases, and project-based learning.

Tim is widely recognized for simplifying complex technical concepts and making advanced AI technologies accessible to both technical and non-technical audiences. His sessions emphasize practical implementation, business value creation, and enterprise-ready solution design.

Through this Forward Deployed Engineering (FDE) program, Tim aims to equip participants with the skills needed to work directly with clients, design AI-driven solutions, build enterprise-grade applications, and successfully deliver modern AI transformation initiatives from concept to production.

Live Sessions Price:

For LIVE sessions – Offer price after discount is 37~~0 USD, 259 ~~USD,~~~~ 169 USD, or 29~~000 INR, 35000,~~ 14900 Rupees

Enroll For Free Demo

Weekend Batch:

Free Demo On:

Indian Timings: 8th August @ 8:30 PM – 9:30 PM (IST)/

U.S Timings: 8th August @ 11:00 AM – 12:00 PM (EST)/

U.K Timings: 8th August @ 4:00 PM – 5:00 PM (BST)

Class Schedule:

For Participants in India: Every Saturday & Sunday @ 8:30 PM – 10 PM (IST)/

For Participants in the US: Every Saturday & Sunday @ 11:00 AM – 12:30 PM (EST)/

For Participants in the UK: Every Saturday & Sunday @ 4:00 PM – 5:30 PM (BST)

Payment Options

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🏦 Bank Account Details:

Account Name : ISHA TRAINING SOLUTIONS

Account Number : 50200113278955

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Branch : NFC Road Branch Moulali, Hyderabad

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What Our Students Say About the Trainer:

One of the best AI training programs I have attended. The trainer explained complex topics such as LLMs, Prompt Engineering, RAG, and AI Agents in a simple and practical manner. The labs and real-world use cases helped me gain confidence in applying these concepts. – Samiksha Lokhande

The Forward Deployed Engineer (FDE) program provided a perfect blend of theory and practical implementation. The sessions were highly interactive and focused on solving real business problems using modern AI technologies. I particularly enjoyed learning about AI Agents, Agentic Workflows, RAG architectures, and Enterprise AI Delivery. The trainer’s ability to connect concepts with real-world scenarios made the learning experience extremely valuable. This course has significantly improved my understanding of Enterprise AI and its practical applications. –Prathima

A great program for anyone looking to learn GenAI, RAG, and AI Agents from scratch. – Vivek Dhiran

Tim Chowdary’s teaching style is exceptional. He explains advanced AI concepts in a simple and structured way while sharing valuable industry insights. The course is packed with practical examples, hands-on exercises, and real-world use cases, making it highly beneficial for both beginners and experienced professionals. – Fatima Ali

The course content is well-structured and covers everything from Generative AI fundamentals to advanced RAG and Agentic AI concepts. The hands-on approach made learning easy and effective. – Michael Johnson

Prerequisites:

Basic understanding of Software Engineering, Programming, or Data Engineering concepts.
Familiarity with Python, APIs, Databases, or Cloud fundamentals is beneficial but not mandatory.
No prior AI experience is required; the course starts with Generative AI fundamentals and progresses to advanced topics through hands-on labs and projects.

Who can enroll for this course:

Software Engineers (Backend / Frontend / Full Stack)
DevOps Engineers
Cloud Engineers (AWS / Azure / GCP)
System Administrators
Technical Support / Application Support Engineers
QA / Test Engineers (Manual & Automation)
Technical Consultants
B.Tech / B.E (CSE, IT, ECE, etc.) students
B.Sc / BCA (Computer Science / IT) students
MCA / M.Tech students
Final-year students
Non-IT professionals with basic programming knowledge and IT understanding

Salient Features:

30+ Hours of Live Training along with recorded videos
1 Year Access to Session Recordings
Course Completion Certificate

What will I learn by the end of this course?

Understand Generative AI, LLMs, Prompt Engineering, and AI application development.
Build RAG (Retrieval-Augmented Generation) solutions using Embeddings and Vector Databases.
Develop AI Agents, Multi-Agent Systems, and AI-powered automation workflows.
Work with LangChain, LangGraph, MCP (Model Context Protocol), and AI orchestration frameworks.
Design and implement Data Pipelines, AI Workflows, and Enterprise AI solutions.
Use AI-assisted development tools for coding, testing, debugging, and rapid prototyping.
Deploy and manage AI applications using Cloud, Docker, and CI/CD practices.
Gain hands-on experience through labs, real-world use cases, and a mini project.
Learn how to translate business requirements into scalable Enterprise AI solutions.
Prepare for roles such as Forward Deployed Engineer (FDE), AI Engineer, GenAI Engineer, and AI Solutions Consultant.

Course Syllabus:

Section 1 — FDE Foundations & Mindset

What is a Forward Deployed Engineer?
FDE vs Software Engineer, Data Engineer, ML Engineer, Solutions Architect
Origins of the role — Palantir, Databricks, OpenAI, Anthropic, Scale AI
The four technical pillars: Data, AI, Software, Cloud & Platform
The four core skills: Critical Thinking, Problem Solving, Decision Making, Communication
The forces in orbit — scoping, ambiguity, cost, customer environment, scalability, governance
Capability x Context — why context is the exponent
Customer obsession, ownership mentality and bias for action
Comfort with ambiguity and first-principles thinking
Common mistakes FDEs make in the first 90 days

Section 2 — Critical Thinking

Critical thinking fundamentals — the 8 traits (Analytical, Objective, Logical, Skeptical, Inquisitive, Open-minded, Reflective, Decisive)
Critical thinking rules — separating symptom from root cause
RED Critical Thinking Framework (Recognise assumptions, Evaluate arguments, Draw conclusions)
Fishbone / Ishikawa root cause analysis
5 Whys and causal chain analysis
Managing assumptions and assumption registers
Asking the right questions — discovery questioning technique
Risk identification and pre-mortem analysis
Cognitive biases in engineering decisions
Eisenhower Matrix and prioritisation under pressure
Trade-off reasoning with incomplete information
Decision making — reversible vs irreversible decisions

Section 3 — Problem Solving & Structured Thinking

Converting vague business problems into technical requirements
Scoping under uncertainty — bounding the problem before coding
Estimation techniques for ambiguous work
SCAMPER for solution ideation
IDEA framework for structured problem solving
Rapid prototyping — 0 to working demo
Delivering iterative value — thin vertical slices
Prioritisation under constrained time and budget
Production incident response and debugging under pressure
Unblocking yourself in a hostile environment (no docs, no access, no data)

Section 4 — Communication & Stakeholder Management

Communicating with non-technical stakeholders
Problem translation — business pain to technical spec and back
Four Gates of thoughtful communication (True, Kind, Necessary Now, Makes a Difference)
PREP framework for structured verbal responses
Running discovery workshops and technical interviews
Technical storytelling for executives
Rapid demo culture — show, do not tell
Communicating trade-offs and saying no well
Building trust with stakeholders on a live engagement
Written communication — status reports, decision logs, handover docs
Cross-team collaboration and managing up

Section 5 — Generative AI Foundations

What is Generative AI? AI vs ML vs Deep Learning vs GenAI
Discriminative vs generative models
Neural networks and transformer architecture (practitioner view)
Attention mechanisms — self-attention and multi-head attention
Positional encoding
Tokens and tokenisation
Context window, memory and session management
Pre-training vs fine-tuning vs RAG
Open vs closed models — when to use what
Popular LLMs — Claude, GPT, Gemini, LLaMA, Mistral
Reasoning models and extended thinking
Model scaling laws and capability trends
Hallucination — why it happens and first-line defenses
Cost, latency and quality trade-offs
Lab: Build your first AI app (API call + prompt + parsed response)

Section 6 — Prompt & Context Engineering

Prompt engineering — the FDE superpower
Markdown and XML structured prompt frameworks
Zero-shot, one-shot and few-shot prompting
Chain-of-thought, prompt chaining and multi-turn design
System prompts and role design
Structured output — JSON mode and schema enforcement
Model controls — temperature, top-k, top-p, max tokens, stop sequences
Enterprise prompt patterns — classification, extraction, summarisation, routing
Prompt evaluation and iteration methodology
Prompt guardrails and injection defenses
Context engineering vs prompt engineering
The context window as a budget — anatomy of context
Short-term vs long-term memory; retrieval as context
Compaction, summarisation and tool-result curation
Failure modes — lost-in-the-middle and context rot
Mini project: Enterprise prompt library

Section 7 — Embeddings & Vector Databases

What are embeddings? Dimensionality and vector space
Embedding models — OpenAI, Cohere, Sentence Transformers
Similarity metrics — cosine, dot product, Euclidean
Chunking strategies for enterprise documents
Chunk overlap and context preservation
Indexing methods — flat, IVF, HNSW
Vector databases — Pinecone, FAISS, Weaviate, pgvector
Vector store operations — CRUD, upserts and re-indexing
Retrieval performance and cost optimisation
Lab: Index a document set and run semantic search

Section 8 — Retrieval-Augmented Generation (RAG)

What is RAG and why FDEs rely on it
RAG architecture — ingestion, retrieval, generation
Data ingestion pipelines — PDFs, databases, APIs, SharePoint
Embedding pipeline design and incremental refresh
Retrieval strategies — top-k, MMR, re-ranking
Metadata filtering and contextual retrieval
Advanced RAG — self-query, HyDE, corrective RAG, multi-hop, GraphRAG
RAG vs fine-tuning — decision criteria
RAG evaluation — faithfulness, answer relevance, context precision and recall
Citations and source attribution
Mini project: Enterprise knowledge assistant with RAG

Section 9 — AI Agents & Tool Use

What is an AI agent? Perception, reasoning, action
Agent architecture — planner, memory, tools
Tool calling and function execution
ReAct pattern — reason plus act loop
Agentic design patterns — planning, reflection, reflexion
AI workflows vs agentic AI — when each fits
Single-agent vs multi-agent trade-offs
Multi-agent architecture — supervisor-worker and peer patterns
Agent frameworks — LangChain, LangGraph, CrewAI
Agent observability, tracing and debugging
Lab: Research agent with tool calling

Section 10 — Model Context Protocol (MCP) & Integrations

What is MCP and why it matters for enterprise integration
MCP architecture — hosts, clients and servers
MCP primitives — resources, tools, prompts, sampling
Transport layers — stdio vs SSE / HTTP
MCP vs function calling vs REST API
Build / invoke an MCP server for enterprise tools
Real-world MCP integrations — filesystem, Git, Slack, browser, databases
AI workflow and automation patterns for enterprise operations

Section 11 — Fine-Tuning & Model Customization

When to fine-tune vs prompt vs RAG
Supervised fine-tuning and instruction tuning
LoRA and PEFT techniques
Dataset preparation, splits and leakage control
Model evaluation metrics for customised models
Cost, safety and bias considerations in customisation

Section 12 — GenAI Evaluation, Observability & Guardrails

AI observability — what to monitor and why
Tracing, logging and metrics for AI applications
Evaluation frameworks — LLM-as-judge and RAGAS
Golden datasets, regression suites and CI for prompts
Guardrails — input and output validation, safety filters
Prompt injection, jailbreaks and AI security threats
AI red teaming and adversarial testing
Hallucination mitigation strategies in production
Human-in-the-loop system design
Lab: Add observability and guardrails to a RAG pipeline

Section 13 — GenAI Governance, Risk & Responsible AI

AI safety fundamentals and risk taxonomy for GenAI
Bias, fairness and model evaluation for harm
Data privacy, PII handling and compliance (GDPR, DPDP, HIPAA)
Enterprise AI governance frameworks and approval gates
AI regulation and compliance landscape (EU AI Act and beyond)
Responsible AI in client engagements — practical guardrails from day one

Section 14 — GenAI Cost, Performance & Optimization

Cost drivers in LLM applications — tokens, calls, context, retries
Prompt caching and context reuse
Semantic caching and response caching
Batch processing and async patterns
Model routing and cascading (small model first, escalate on failure)
Latency optimisation — streaming, parallelism, prefetch
Rate limiting, retries and backoff strategies
Scaling LLM applications and capacity planning
Building a cost dashboard and unit economics per feature

Section 15 — AI-Assisted Software Engineering

The AI-first software development lifecycle
Claude Code — agentic coding workflow, CLAUDE.md and permissions
VS Code and Claude integration
Rapid prototyping with AI — 0 to working demo
AI-powered code review and refactoring
AI-assisted debugging and test generation
Prompt-driven architecture and design docs
Software engineering fundamentals for FDEs — APIs, REST, Git, CI/CD
Lab: Build a full feature in 2 hours using AI tools

Section 16 — Data Engineering for FDEs

Data engineering fundamentals for FDEs
ETL vs ELT — the modern approach
Data lake vs data warehouse vs lakehouse
Medallion architecture — bronze, silver, gold
Batch vs streaming — when each makes sense
Data pipelines — ingestion, transformation, loading, orchestration
Data quality, validation and observability
Data governance, lineage and cataloguing (Unity Catalog, Glue Catalog)
Modern data platforms — Databricks, Snowflake, dbt, Spark
Integrating data platforms with AI pipelines
Lab: End-to-end data pipeline with AI transformation

Section 17 — Cloud & Platform Design (AWS Well-Architected Framework)

Cloud-native architecture principles for FDEs
AWS Well-Architected Framework — overview of the 6 pillars
Pillar 1 — Operational Excellence (IaC, runbooks, observability, change management)
Pillar 2 — Security (IAM, least privilege, KMS, Secrets Manager, encryption, WAF
Pillar 3 — Reliability (multi-AZ, DR strategies, RTO/RPO, graceful degradation
Pillar 4 — Performance Efficiency (right-sizing, caching, serverless, scaling)
Pillar 5 — Cost Optimization (Cost Explorer, Trusted Advisor, tagging, savings plans)
Pillar 6 — Sustainability (efficient resource use, region selection)
AWS core services for AI workloads — EC2, S3, Lambda, Bedrock, SageMaker
Networking and isolation — VPC, subnets, security groups, PrivateLink
CI/CD, Git workflows and DevOps for FDEs
Monitoring and observability on AWS — CloudWatch, X-Ray, CloudTrail
Migration strategies — the 6R model

Section 18 — System Design (incl. GenAI System Design)

System design fundamentals and components
System design principles and the evaluation rubric
Monolith vs microservices vs serverless
Scalability — load balancing, sharding, partitioning, queues
Reliability — redundancy, failover, circuit breakers, graceful degradation
End-to-end LLM application architecture
Caching, rate limiting and multi-tenancy for AI systems
Design: Option A Customer support AI assistant
Design: Option B AI-powered recruiting platform
Live mock system design interview

Section 19 — Enterprise AI Patterns & Case Studies

Pattern: Knowledge assistant over internal wikis and documents
Pattern: AI operations agent — ticket routing and escalation
Pattern: AI for migration and modernization projects
Case study: FDE at Databricks — lakehouse migration
Case study: FDE at an AI startup — rapid MVP delivery
Case study: Enterprise RAG implementation and production incident response

Section 20 — FDE Delivery & Customer Engagement

Discovery to delivery — the end-to-end engagement lifecycle
Scoping documents, statements of work and success criteria
Working inside the customer environment — access, security, politics, legacy systems
Cost conversations and ROI justification with clients
Handover, enablement and automation so value persists after you leave
Daily habits of exceptional FDEs
FDE interview preparation — behavioural and technical
Building an FDE portfolio for job applications

Section 21 — Capstone Project

Option A: Enterprise knowledge assistant (RAG + agents)
Option B: Spend Analytics

Events

Forward Deployed Engineer (FDE) Master Program: GenAI, RAG, Agents & Enterprise AI – Day2

Event Information