Transition Into High-Paying Forward Deployed AI Engineering Roles at Top Companies

Built for experienced engineers want to become job-ready for Forward Deployed AI Engineering roles

Taught live and mentored by FDEs & AI Engineers working at MAANG & Top Companies.
In-depth, builder-oriented FDE curriculum that bridges deep Agentic AI systems design with customer-facing delivery.
Includes interview preparation designed for Forward Deployed AI Engineering roles across MAANG, product companies, GCCs & top startups.

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Program Overview

Who This Is Built For

Customer Engineers, Solutions Architects, Solutions Engineers, and AI Solution Consultants
Backend, Full Stack, Platform, DevOps & Software Engineers
Machine Learning Engineers (MLEs), Data Engineers, and Data Scientists
Tech Leads, TPMs, Engineering Managers, and Senior Engineers who still code

Program Duration

23 weeks across four structured blocks
Progresses from agentic foundations to full FDE delivery
Designed to run alongside a full-time engineering role

Live Learning

Live interactive sessions on concepts and system design
A guided, real-world live project built every week
Dedicated AI & FDE interview preparation

Projects

8 live, expert-guided, end-to-end build projects
An enterprise FDE capstone across three cloud stacks
Real customer AI systems built for real-world use - not pilot demos.

Instructors

FDE practitioners, Agentic AI Engineers, Solutions Architects from MAANG & Top-Tier Tech Companies
Practitioners who build and operate agentic systems in production
Guidance grounded in real customer-delivery decisions

What You’ll Build and Learn

Agentic system design, RAG, and multi-agent orchestration
Enterprise wiring: APIs, MCP servers, RBAC, and governance
Evaluation, observability, safety guardrails, and cost control

Multi-Stack Capstone Coverage

Primary Stack: OpenAI Agent SDK + AWS Bedrock AgentCore
Alternate Stacks: Google ADK + Vertex AI; Claude Agents SDK + Azure
Graduate ready to ship AI agents across Azure, Google Cloud, and AWS

FDE Explained: Who, How & Why?

Who is an FDE?

An FDE partners with enterprise customers to design, build, deploy, and scale AI solutions that solve real business problems.

How does an FDE work?

FDEs own the end-to-end customer journey - from discovery and solution design to implementation, deployment, adoption, and knowledge transfer.

Why is this role needed?

Building AI is only the beginning. Organizations need engineers who can successfully deploy, integrate, and operate AI systems in complex enterprise environments.

Alumni

Highest offer

₹ 0 Cr

Average compensation package

₹ 0 LPA

Average ROI on course price

0 x - 7x

30+ Tools & Tech You’ll Learn

Everything You Need to Become a Forward Deployed AI Engineer

Forward Deployed AI Engineering sits at the intersection of Agentic AI engineering and customer delivery. Whether your background is in software, data, cloud, or customer-facing engineering, this program helps you build the complete skill stack – from building and deploying AI solutions to customer delivery – needed to transition into Forward Deployed AI Engineering roles.

The AI-Proof Bet

Deep enough technically to build the systems
Human enough to stay essential as coding is automated
Expertise that stays in demand in an AI-powered industry

Bridging the Gap

Customer-facing engineers build AI engineering depth
Software and Data Engineers develop customer delivery skills
Graduate with the complete Forward Deployed AI Engineering skill stack

Comprehensive Forward Deployed AI Engineering Program

End-to-end curriculum covering AI engineering and enterprise deployment
Learn AI engineering, customer delivery, and interview preparation in one program
Built to prepare engineers for high-impact Forward Deployed AI Engineering roles

Production Systems, Not Just Demos

Move past isolated LLM, RAG, and framework demos
Build end-to-end, production-grade agentic systems
Orchestration, safety, evaluation, cost control, and deployment

The Complete FDE Lifecycle

Learn the distinctly FDE craft, not just AI engineering
Customer discovery, problem framing, scoping, and commercials
Enterprise wiring, governance, operations, and customer handover

Interview Preparation & Career Support

Six weeks of dedicated AI Engineering and FDE interview preparation
AI system design, decomposition cases, and AI-assisted coding interviews
Aligned with how leading AI companies (OpenAI, Anthropic, etc.), GCCs, and top-tier companies actually hire

Learn from AI Leaders at MAANG & Top-Tier Tech Firms

Taught live by current and former AI Engineers and Forward Deployed Engineers (FDEs)
Mentorship from professionals building production AI systems
Practical insights gained from real-world enterprise AI deployments

Multi-Stack Capstone Coverage

Live classes and a capstone project using OpenAI Agents SDK and AWS Bedrock AgentCore
Alternate-stack delivery with Google ADK + Vertex AI and Claude Agents SDK + Azure
Graduate ready to build and deploy AI agents across AWS, Google Cloud, and Azure

This is a living curriculum, continuously updated to reflect how Forward Deployed AI Engineering is practiced in 2026 and beyond.

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Detailed Curriculum: Forward Deployed Engineering Program

AI Engineering Spine

Week 1: Agentic AI Foundations & Reflex Agents

The agent equation: prompt, tools, memory, and LLM
The ReAct loop and the five core agentic design patterns
Agentic-vs-autonomous decision-making and prompt engineering

Live Project: CRM Lead Qualifier Agent

Outcome: Decide when a task needs an agent – and wire up the first one that works.

Weeks 2–3: RAG-Powered Knowledge Agents

Retrieve → augment → generate pipelines with LangChain LCEL
Multi-turn RAG with history and hallucination prevention
Retrieval and generation metrics: Precision@K, groundedness

Live Project: Grounded IT Support Knowledge Assistant
Outcome: Ship a RAG agent that answers only from its sources and proves it with metrics.

Week 4: Multi-Agent Systems (Planner–Executor–Critic)

Role-based design: orchestrator, planner, synthesizer
Task decomposition, routing, and delegation
LangGraph state, nodes, edges, and checkpointer persistence

Live Project: Multi-Agent Travel Planner
Outcome: Split a task that breaks one agent across a team that doesn’t.

Week 5: Conversational & Multimodal Agents

Cascaded STT→LLM→TTS vs. realtime speech-to-speech trade-offs
Reusable LangGraph subgraphs for coordination
Human-in-the-loop approve, review-and-edit, and interrupt patterns

Live Project: Voice-Enabled E-Commerce Assistant with HITL
Outcome: Put a human in the loop without killing the conversation’s UX.

Week 6: Agent Communication Protocols (MCP, A2A, ACP)

Structured tool access via MCP and FastMCP servers
Reliable messaging with finite state machines and validated transitions
Networked agents over the A2A protocol via Google ADK

Live Project: Real Estate Negotiation Simulator
Outcome: Kill the ten failure modes that come from agents talking in free text.

Week 7: Hybrid Search & Retrieval

Sparse vs. dense vectors; k-NN, ANN, and HNSW
SPLADE for learned lexical matching
Hybrid search with Reciprocal Rank Fusion in Qdrant

Live Project: Hybrid Product Search Agent (SPLADE + BGE + RRF)
Outcome: Beat pure-vector recall by fusing lexical and semantic retrieval.

Week 8: Agent Observability, Evaluation & Safety

LangSmith tracing and eval datasets from curated traces
LLM-as-judge, DeepEval metrics, and Guardrails AI
PII redaction with Presidio; cost control via routing, caching, and batching

Live Project: Production-Ready Fintech Support Agent
Outcome: Cut an agent’s cost-per-query while proving its quality didn’t drop.

Week 9: Fine-Tuning & Domain Adaptation

The prompt-vs-RAG-vs-fine-tune escalation framework
The PEFT landscape: LoRA, QLoRA, prefix tuning, and adapters
4-bit quantization, TRL SFTTrainer, and deployment to the HF Hub

Live Project: Fine-Tuned Healthcare Q&A Agent

Outcome: Know when fine-tuning beats prompting – then train and ship an adapter.

Weeks 10-11: Capstone - Enterprise Multi-Agent System

Own a real enterprise problem from architecture to build
Integrate retrieval, orchestration, evals, safety, and cost monitoring
Defend reliability, latency, and cost the way production systems are reviewed

Live Project: LangGraph, LangChain, Streamlit, AWS
Outcome: Stand up a production-grade agentic system you can defend in review.

AI-Assisted Coding

Week 12: Pair Programming with Claude

Real interview intent and problem-solving mindset in the AI era
The structured 5-phase AI-assisted coding framework
The Claude ecosystem: Skills, Projects, Artifacts, and Claude Code

Live project: Building a personalized DSA coach
Outcome: Pair with AI the way the new pair-coding interview round expects.

FDE Spine

Week 13: Customer Discovery & Scoping (FDE capstone starts)

What the FDE role is and how it differs from SWE, SA, and MLE
The 90-minute discovery script and “show me how you do this today”
Stakeholder mapping, RACI, and decision-log discipline

Live project: FDE capstone scenario kicks off
Outcome: Turn a vague AI ask into scoped options with eval-anchored success criteria.

Week 14: SoW, Pricing & Solution Architecture

The 4-milestone estimation framework with risk buffers
SoW writing with eval-anchored acceptance criteria and IP/data clauses
Pricing models and a defensible stack-choice decision matrix

Live project: Capstone work: SoW + pricing rationale + architecture diagram
Outcome: Price and architect an engagement you can defend to a procurement officer.

Week 15: Building Production Agents (OpenAI SDK + AWS Bedrock Agentcore)

Agents, Handoffs, Guardrails, Sessions, and sandboxed execution
Foundry Agent Service vs. Hosted Agents vs. Container Apps
Production hardening, the four multi-agent failure modes, and RAG at scale

Live project: Capstone work: harden the build to production

Outcome: Run hardened production agents on AWS under real load.

Week 16: APIs, MCP Servers & RBAC

Designing APIs as agent tools (FastAPI, OpenAPI, and idempotency)
Building and hardening MCP servers with per-tenant scoping and audit logs
Auth patterns: OAuth 2.0, Entra service principals, and managed identities

Live project: Multi-Tenant MCP Server with RBAC
Outcome: Wire an agent into customer systems with access control at the tool boundary.

Week 17: Evals, Observability & Handover (capstone completion)

Data governance, residency, tenant isolation, and the autonomy ladder
Compliance literacy: SOC 2, HIPAA + BAA, FedRAMP, ISO 27001, and GDPR
Eval suites as a CI deploy gate, an incident fire-drill, and clean handover

Live project: Capstone wraps across all three stacks by end of Week 17
Outcome: Gate deploys on an eval suite and hand the system off without you in the loop.

FDE Interview Prep

Weeks 18–21: Agentic AI System Design Practice

Research agents: planning, tools, and guardrails under interview conditions
Reliable text-to-SQL agents over large schemas
Multi-agent coordination, shared memory, and self-improving verification loops

Live project: Common themes: reliability, evaluation, and cost
Outcome: Design and defend agentic systems live in the system-design round.

Week 22: Decomposition & Case Study Interviews

The Palantir-origin FDE decomposition / case round
Clarify ambiguity, decompose, propose a 4-week scope, and name the riskiest assumption
A 10+ scenario library, the AI-assisted pair-coding round, and architecture interrogation

Live project: Practice cases across multiple industries
Outcome: Run the FDE case round and hold your architecture under hostile questioning.

Week 23: Behavioral & Procurement–Security Interviews

The STAR+ framework and 16 FDE-specific behavioral prompts
Communicating trade-offs and delivering bad news without losing credibility
A procurement/security simulation and a 15-question security bank

Live project: Program closing: portfolio walk-through and recruiter-outreach plan
Outcome: Tell sharp customer-facing stories and survive a security cross-examination.

Bonus Content (Self-Paced)

Foundations track:

AI-driven development foundations, Python essentials, prompting & tooling, multi-agent systems, LLM frameworks (MCP, A2A, ADK), and Development → Deployment (Docker, FastAPI, Kubernetes basics, scalable RAG).

Career track:

DSA (Sorting, Recursion, Trees, Graphs, DP), Resume & LinkedIn Masterclass, Behavioral Interview Strategies, and Offer Negotiation Workshop.

The curriculum is constantly updated as per industry developments and is subject to change.

Detailed Curriculum: Forward Deployed Engineering Program

AI Engineering Spine

Week 1: Agentic AI Foundations & Reflex Agents

The agent equation: prompt, tools, memory, and LLM
The ReAct loop and the five core agentic design patterns
Agentic-vs-autonomous decision-making and prompt engineering

Live Project: CRM Lead Qualifier Agent

Outcome: Decide when a task needs an agent – and wire up the first one that works.

Weeks 2–3: RAG-Powered Knowledge Agents

Retrieve → augment → generate pipelines with LangChain LCEL
Multi-turn RAG with history and hallucination prevention
Retrieval and generation metrics: Precision@K, groundedness

Live Project: Grounded IT Support Knowledge Assistant
Outcome: Ship a RAG agent that answers only from its sources and proves it with metrics.

Week 4: Multi-Agent Systems (Planner–Executor–Critic)

Role-based design: orchestrator, planner, synthesizer
Task decomposition, routing, and delegation
LangGraph state, nodes, edges, and checkpointer persistence

Live Project: Multi-Agent Travel Planner
Outcome: Split a task that breaks one agent across a team that doesn’t.

Week 5: Conversational & Multimodal Agents

Cascaded STT→LLM→TTS vs. realtime speech-to-speech trade-offs
Reusable LangGraph subgraphs for coordination
Human-in-the-loop approve, review-and-edit, and interrupt patterns

Live Project: Voice-Enabled E-Commerce Assistant with HITL
Outcome: Put a human in the loop without killing the conversation’s UX.

Week 6: Agent Communication Protocols (MCP, A2A, ACP)

Structured tool access via MCP and FastMCP servers
Reliable messaging with finite state machines and validated transitions
Networked agents over the A2A protocol via Google ADK

Live Project: Real Estate Negotiation Simulator
Outcome: Kill the ten failure modes that come from agents talking in free text.

Week 7: Hybrid Search & Retrieval

Sparse vs. dense vectors; k-NN, ANN, and HNSW
SPLADE for learned lexical matching
Hybrid search with Reciprocal Rank Fusion in Qdrant

Live Project: Hybrid Product Search Agent (SPLADE + BGE + RRF)
Outcome: Beat pure-vector recall by fusing lexical and semantic retrieval.

Week 8: Agent Observability, Evaluation & Safety

LangSmith tracing and eval datasets from curated traces
LLM-as-judge, DeepEval metrics, and Guardrails AI
PII redaction with Presidio; cost control via routing, caching, and batching

Live Project: Production-Ready Fintech Support Agent
Outcome: Cut an agent’s cost-per-query while proving its quality didn’t drop.

Week 9: Fine-Tuning & Domain Adaptation

The prompt-vs-RAG-vs-fine-tune escalation framework
The PEFT landscape: LoRA, QLoRA, prefix tuning, and adapters
4-bit quantization, TRL SFTTrainer, and deployment to the HF Hub

Live Project: Fine-Tuned Healthcare Q&A Agent

Outcome: Know when fine-tuning beats prompting – then train and ship an adapter.

Weeks 10-11: Capstone - Enterprise Multi-Agent System

Own a real enterprise problem from architecture to build
Integrate retrieval, orchestration, evals, safety, and cost monitoring
Defend reliability, latency, and cost the way production systems are reviewed

Live Project: LangGraph, LangChain, Streamlit, AWS
Outcome: Stand up a production-grade agentic system you can defend in review.

AI-Assisted Coding

Week 12: Pair Programming with Claude

Real interview intent and problem-solving mindset in the AI era
The structured 5-phase AI-assisted coding framework
The Claude ecosystem: Skills, Projects, Artifacts, and Claude Code

Live project: Building a personalized DSA coach
Outcome: Pair with AI the way the new pair-coding interview round expects.

FDE Spine

Week 13: Customer Discovery & Scoping (FDE capstone starts)

What the FDE role is and how it differs from SWE, SA, and MLE
The 90-minute discovery script and “show me how you do this today”
Stakeholder mapping, RACI, and decision-log discipline

Live project: FDE capstone scenario kicks off
Outcome: Turn a vague AI ask into scoped options with eval-anchored success criteria.

Week 14: SoW, Pricing & Solution Architecture

The 4-milestone estimation framework with risk buffers
SoW writing with eval-anchored acceptance criteria and IP/data clauses
Pricing models and a defensible stack-choice decision matrix

Live project: Capstone work: SoW + pricing rationale + architecture diagram
Outcome: Price and architect an engagement you can defend to a procurement officer.

Week 15: Building Production Agents (OpenAI SDK + AWS Bedrock Agentcore)

Agents, Handoffs, Guardrails, Sessions, and sandboxed execution
Foundry Agent Service vs. Hosted Agents vs. Container Apps
Production hardening, the four multi-agent failure modes, and RAG at scale

Live project: Capstone work: harden the build to production

Outcome: Run hardened production agents on AWS under real load.

Week 16: APIs, MCP Servers & RBAC

Designing APIs as agent tools (FastAPI, OpenAPI, and idempotency)
Building and hardening MCP servers with per-tenant scoping and audit logs
Auth patterns: OAuth 2.0, Entra service principals, and managed identities

Live project: Multi-Tenant MCP Server with RBAC
Outcome: Wire an agent into customer systems with access control at the tool boundary.

Week 17: Evals, Observability & Handover (capstone completion)

Data governance, residency, tenant isolation, and the autonomy ladder
Compliance literacy: SOC 2, HIPAA + BAA, FedRAMP, ISO 27001, and GDPR
Eval suites as a CI deploy gate, an incident fire-drill, and clean handover

Live project: Capstone wraps across all three stacks by end of Week 17
Outcome: Gate deploys on an eval suite and hand the system off without you in the loop.

FDE Interview Prep

Weeks 18–21: Agentic AI System Design Practice

Research agents: planning, tools, and guardrails under interview conditions
Reliable text-to-SQL agents over large schemas
Multi-agent coordination, shared memory, and self-improving verification loops

Live project: Common themes: reliability, evaluation, and cost
Outcome: Design and defend agentic systems live in the system-design round.

Week 22: Decomposition & Case Study Interviews

The Palantir-origin FDE decomposition / case round
Clarify ambiguity, decompose, propose a 4-week scope, and name the riskiest assumption
A 10+ scenario library, the AI-assisted pair-coding round, and architecture interrogation

Live project: Practice cases across multiple industries
Outcome: Run the FDE case round and hold your architecture under hostile questioning.

Week 23: Behavioral & Procurement–Security Interviews

The STAR+ framework and 16 FDE-specific behavioral prompts
Communicating trade-offs and delivering bad news without losing credibility
A procurement/security simulation and a 15-question security bank

Live project: Program closing: portfolio walk-through and recruiter-outreach plan
Outcome: Tell sharp customer-facing stories and survive a security cross-examination.

Bonus Content (Self-Paced)

Foundations track:

Career track:

DSA (Sorting, Recursion, Trees, Graphs, DP), Resume & LinkedIn Masterclass, Behavioral Interview Strategies, and Offer Negotiation Workshop.

The curriculum is constantly updated as per industry developments and is subject to change.

Live Guided Projects

Built with the instructor during live sessions, step-by-step code-along builds.

CRM Lead Qualifier Agent

Your first LLM-powered agent. Learn how an LLM uses function calling as its reasoning engine, how tools (domain lookup, CRM history, lead scoring) shape behavior, and how a think–act–observe loop drives real decisions.

SupportDesk-RAG

A production-ready RAG system for IT support that answers strictly from retrieved ticket data. Covers OpenAI embeddings, chunking, five LlamaIndex indexing approaches, a LangChain LCEL pipeline, anti-hallucination safeguards, two-layer evaluation, and an agentic RAG extension with memory.

Multi-Agent Travel Planner

An Orchestrator → Search → Itinerary Planner → Synthesizer workflow where specialized agents search flights and hotels, generate itineraries, and synthesize recommendations. Built with LangGraph, LangChain, Tavily, SerpAPI.

AxiomCart - Voice-Enabled Shopping Assistant

A stateful, voice-enabled assistant across a LangGraph StateGraph with RAG product discovery, order tracking with HITL interrupts, parallel dispatch, MemorySaver checkpointing, and a Whisper + OpenAI TTS pipeline.

Real Estate Negotiation Simulator

A buyer–seller system where agents communicate via typed Pydantic schemas. Progress from a broken build exposing ten failure modes to a robust architecture with FSM terminal states, MCP-grounded tools, LangGraph routing, and true A2A transport via Google ADK

Hybrid Product Search Agent

Lexical precision plus semantic understanding over Amazon’s ESCI dataset. Generate dual embeddings with SPLADE and BGE-Large, index in Qdrant with HNSW, and merge via Reciprocal Rank Fusion.

Production-Ready Fintech Support Agent

A supervisor + specialist system with full production hardening: LangSmith tracing, DeepEval metrics, Guardrails AI validators, Presidio PII redaction, and tiktoken-powered cost-per-query dashboards.

Domain-Specific Fine-Tuned Agent

A Healthcare Q&A agent: fine-tune a 4-bit quantized Qwen2.5-1.5B-Instruct with QLoRA, deploy the LoRA adapter to the HF Hub, and evaluate side-by-side against the base model in LangSmith.

Projects are subject to change as per industry inputs.

Live Guided Projects

Built with the instructor during live sessions, step-by-step code-along builds.

CRM Lead Qualifier Agent

Your first LLM-powered agent. Learn how an LLM uses function calling as its reasoning engine, how tools (domain lookup, CRM history, lead scoring) shape behavior, and how a think–act–observe loop drives real decisions.

SupportDesk-RAG

A production-ready RAG system for IT support that answers strictly from retrieved ticket data. Covers OpenAI embeddings, chunking, five LlamaIndex indexing approaches, a LangChain LCEL pipeline, anti-hallucination safeguards, two-layer evaluation, and an agentic RAG extension with memory.

Multi-Agent Travel Planner

An Orchestrator → Search → Itinerary Planner → Synthesizer workflow where specialized agents search flights and hotels, generate itineraries, and synthesize recommendations. Built with LangGraph, LangChain, Tavily, SerpAPI.

AxiomCart - Voice-Enabled Shopping Assistant

A stateful, voice-enabled assistant across a LangGraph StateGraph with RAG product discovery, order tracking with HITL interrupts, parallel dispatch, MemorySaver checkpointing, and a Whisper + OpenAI TTS pipeline.

Real Estate Negotiation Simulator

A buyer–seller system where agents communicate via typed Pydantic schemas. Progress from a broken build exposing ten failure modes to a robust architecture with FSM terminal states, MCP-grounded tools, LangGraph routing, and true A2A transport via Google ADK

Hybrid Product Search Agent

Lexical precision plus semantic understanding over Amazon’s ESCI dataset. Generate dual embeddings with SPLADE and BGE-Large, index in Qdrant with HNSW, and merge via Reciprocal Rank Fusion.

Production-Ready Fintech Support Agent

A supervisor + specialist system with full production hardening: LangSmith tracing, DeepEval metrics, Guardrails AI validators, Presidio PII redaction, and tiktoken-powered cost-per-query dashboards.

Domain-Specific Fine-Tuned Agent

A Healthcare Q&A agent: fine-tune a 4-bit quantized Qwen2.5-1.5B-Instruct with QLoRA, deploy the LoRA adapter to the HF Hub, and evaluate side-by-side against the base model in LangSmith.

Projects are subject to change as per industry inputs.

Production-Grade Capstone Projects

Pick from up to 7 production-grade FDE engagements or build your own.

ClaimSense AI

What you’ll build:
A full FDE engagement for a regional auto insurer – a multi-agent + MCP + RAG system that reads claim files (police reports, photos, repair estimates), surfaces high-confidence first decisions to adjusters, and routes edge cases to human review.

Tools & concepts:
OpenAI Agents SDK + AWS Bedrock Agentcore, FastMCP, audit logging, multi-agent systems, RAG

PriorAuth AI

What you’ll build:
A full FDE engagement at a hospital network stuck at a 9-day prior-auth turnaround after a HIPAA-blocked pilot. Run discovery, write the SoW, then build a co-pilot that reads the chart note, retrieves payer policy, drafts the auth request with citations, and routes ambiguous cases to a reviewer.

Tools & concepts:
OpenAI Agents SDK + AWS Bedrock Agentcore, FastMCP, SSO, audit logging, multi-agent systems, RAG (alternate stacks: Google ADK + Vertex; Claude SDK + Azure)

AI Finance Assistant

What you’ll build:
A six-agent system delivering context-aware investment guidance, portfolio analysis, goal planning, and tax education through a conversational interface.

Tools & concepts:
LangGraph, multi-agent systems, RAG, LLMs, prompt engineering.

AI Content Marketing Assistant

What you’ll build:
A multi-agent platform generating blogs, LinkedIn posts, and visuals with SEO optimization, brand-voice consistency, and platform-specific formatting.

Tools & concepts:
Multi-agent systems, LLMs, SEO basics, multimodal output.

Call Center Intelligence System

What you’ll build:
A seven-stage pipeline that transcribes with speaker diarization, summarizes, and scores each call on a five-dimension QA scorecard, producing compliance flags and downloadable reports.

Tools & concepts:
LangGraph, Whisper, PII redaction, LLMs, report generation.

Multi-Agent Customer Support Assistant

What you’ll build:
A supervisor-coordinated assistant resolving queries against a live relational database, with catalog-search and invoice-lookup agents, identity verification, and anti-hallucination grounding.

Tools & concepts:
Multi-agent systems, LLMs, database integration, memory.

BYOP - Bring Your Own Project

What you’ll build
A personal or professional project of your choice, scoped with mentorship and structured feedback to meet industry standards.

Tools & concepts
Tool and framework selection, best practices, mentorship.

Capstone Projects are subject to change as per industry inputs.

Production-Grade Capstone Projects

Pick from up to 7 production-grade FDE engagements or build your own.

ClaimSense AI

Tools & concepts:
OpenAI Agents SDK + AWS Bedrock Agentcore, FastMCP, audit logging, multi-agent systems, RAG

PriorAuth AI

Tools & concepts:
OpenAI Agents SDK + AWS Bedrock Agentcore, FastMCP, SSO, audit logging, multi-agent systems, RAG (alternate stacks: Google ADK + Vertex; Claude SDK + Azure)

AI Finance Assistant

AI Content Marketing Assistant

Call Center Intelligence System

Tools & concepts:
LangGraph, Whisper, PII redaction, LLMs, report generation.

Multi-Agent Customer Support Assistant

BYOP - Bring Your Own Project

Capstone Projects are subject to change as per industry inputs.

+ Instructors to Train You

Get mentored by AI/ML leaders who are driving Agentic AI innovation at top global companies.

Prateek Singhal

Software Engineer II, Scaling AI Infrastructure

Sandhya Kapoor

SVP, Central Platform, AI Infrastructure, Ex-Meta

Anshaj Khare

Software Engineer II, Agentic AI

Ram Vegiraju

ML Architect @ AWS

Jorge Luna

Principal Forward Deployed Engineering Lead

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Career Session

The IK Experience: What Our Alumni Are Saying

Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.

Venkatesh Babu

Senior Director - ISV/SolEx Technical Partner Management

I highly recommend the Applied GenAI course by Interview Kickstart. The PM path was incredibly well-organized, reshaping my thinking on how to leverage Generative AI in product management. The hands-on approach, insightful curriculum, and experienced instructors made it an outstanding learning experience!

Amit Vaswani

Lead Software Engineer, Highnote

I recently completed the Applied Gen AI course at Interview Kickstart, and I couldn't be more impressed. The course was well-structured into modules, making complex concepts easier to digest. For someone without a strong programming background, I appreciated the beginner Python class they offer for non-programmers - it helped build a solid foundation. The instructors are fantastic and always go the extra mile to ensure every question is answered. They are patient and don’t rush through the material, often allowing classes to run over to ensure everyone fully grasps the concepts. One of my favorite features is the 'Expert Connect,' where you can have 1-on-1 sessions with instructors to clear any doubts. Overall, Interview Kickstart provided an exceptional learning experience, and I highly recommend it to anyone looking to uplevel in their career.

Abhishek Singh

Senior Data Analyst, Jeavio

IK is a GOD-Send to me. If you are looking to upskill or transition in your career, IK is the place to be. I joined Data Science Switchup in November-23 and I am loving the 360 degree experience which IK provides. The instructors are professionals who are currently working in the tier-1 companies so the classes have loads of real-life snippets of their experience which adds true value to students like us. The mock sessions and technical sessions helped me immensely to understand the topic at a deeper level. The Ops Team & Success Coaches are superstars who act like a true friend when you need any sort of assistance.

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IK has been an integral part of my life for last seven years. Since being part of some of the early batches to most recently pursuing their ML track I have done several courses with them. What amazes me is the approachability of the staff, the proactive support team and professionalism. The material is amazing and profound. Omkar and Niloy to name a few they have amazing faculty who are very knowledgeable and are also great teachers. I can keep raving about IK. It’s definitely a boon to software engineers.

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Career Session

FAQs

What is a Forward Deployed AI Engineer, and why is this role growing?

A Forward Deployed AI Engineer role combines AI engineering with customer-facing problem solving. They build, deploy, and scale AI solutions within enterprise environments, working closely with customers to ensure real business impact. As AI adoption accelerates across enterprises, GCCs, product companies, and top startups, the demand for engineers who can bridge technology and business is rapidly increasing.

Who is this program designed for?

Two groups who each bring one half of the FDE skill stack. Customer-facing engineers – Customer Engineers, Solutions Architects, Solutions Engineers – who want AI engineering depth; and coding or data engineers – MLEs, Data Engineers, Data Scientists, backend SWEs, and coding Tech Leads, TPMs, and EMs – who want the customer discovery, scoping, and delivery craft. Both leave with the complete stack.

Do I need prior AI or Machine Learning experience?

No. The program starts by building a strong foundation in modern AI engineering, including agents, RAG, multi-agent systems, evaluations, and enterprise AI concepts. A software engineering background and working knowledge of Python are recommended.

What makes this program different from other AI programs?

Most AI programs focus on building AI applications. This program goes further by teaching you how to design, deploy, integrate, and scale AI solutions in enterprise environments while developing the customer-facing skills required for Forward Deployed AI Engineering roles.

What’s the difference between Live Guided Projects and the Capstone?

Live Guided Projects are built step-by-step with instructors during live sessions. The Capstone is an independent AI project where you apply your learning with mentor guidance, similar to solving a real customer engagement.

How is the program delivered?

The program includes live instructor-led classes, weekly guided projects, hands-on assignments, interview preparation, and self-paced learning resources to reinforce key concepts.

How much time should I plan each week?

Expect to spend around 10–12 hours per week, including live sessions, assignments, projects, and self-paced practice.

What tools and technologies will I learn?

You’ll work with Python, LangChain, LangGraph, LlamaIndex, MCP, OpenAI, Anthropic, Gemini, FastAPI, Docker, AWS, Azure, GCP, vector databases, evaluation frameworks, and enterprise AI deployment tools.

What is multi-stack capstone coverage?

You’ll build your capstone across multiple leading AI ecosystems, helping you gain practical experience with AWS, Azure, and Google Cloud AI platforms so you’re prepared for diverse enterprise environments.

Can I choose my capstone project?

Yes. You can select from industry-inspired AI projects or propose your own idea, with guidance from mentors to ensure it’s technically feasible and industry relevant.

Will I learn when not to use AI agents?

Yes. You’ll learn when agentic AI is the right choice and when traditional software, workflows, or machine learning approaches are more effective, along with the reasoning behind those decisions.

How production-ready are the systems I’ll build?

You’ll build production-inspired AI systems that incorporate evaluation, observability, security, governance, deployment, and production integration practices commonly used in real-world organizations.

Will this help me transition into Forward Deployed AI Engineering roles?

Yes. The program prepares you for Forward Deployed AI Engineering interviews through AI system design, coding, case-based discussions, behavioral preparation, and mock interviews aligned with hiring practices at leading technology companies, GCCs, and AI startups.

How are the instructors qualified?

You’ll learn from current and former AI Engineers, Solutions Architects, and Forward Deployed Engineering practitioners from MAANG and other leading global technology companies with hands-on experience building enterprise AI solutions.

Will there be assignments or exams?

The program is project-driven rather than exam-driven. Every module includes practical assignments and hands-on builds designed to strengthen your engineering and system design skills.

What happens if I miss a live session?

All live sessions are recorded and made available for later viewing. You’ll also have access to instructor support, discussion forums, and learning resources to help you stay on track.