AI Engineering Course: Build AI Systems and Become an AI Forward Deployed Engineer
The AI FDE Fellowship is Menler's 12-week, live online AI engineering course — India's only Claude-first AI Forward Deployed Engineer Fellowship. Build RAG, AI agents and MCP servers on six real Indian cases, deploy them to production, and defend each one to an industry panel. Next batch starts November 14, 2026.
A Forward Deployed Engineer (FDE) embeds with a client, understands their problem, builds the AI system and owns it in production. The Menler AI FDE Fellowship trains this role over 12 weeks in three phases: LLM foundations, Claude Code and RAG (Weeks 1–4); AI agents, MCP, evals and AI security (Weeks 5–8); and production deployment, ROI and a client capstone (Weeks 9–12). It runs live on Saturdays and Sundays — 24 sessions, 48 hours — in pods of five with one mentor for every two pods, costs ₹75,000 including taxes with EMI from ₹4,999 a month, and ends with six deployed builds, six Case Defences, Demo Day and the Menler AI FDE Certification.
The AI FDE Fellowship in numbers
- 100 Engineering Competencies — Every skill an AI FDE needs, assessed one by one.
- 48 Live Hours — Live weekend sessions led by industry mentors.
- 12 Weeks Intensive — A focused programme that fits around work or college.
- 12 Weekly Assignments — One hands-on assignment a week, reviewed by a mentor.
- 6 Production-Grade Projects — Real systems you build, deploy and add to your portfolio.
- 6 Real Case Studies — Real problems from banking, farming, finance, health and retail.
Who this AI engineering course is for
- Students & fresh graduates: Start your career with a deployed AI portfolio.
- Engineers & developers: Go from writing features to shipping AI systems.
- Product, design & delivery: Stop handing AI specs over. Ship it yourself.
- Founders & domain experts: Deploy AI into the problem you know best.
- Freelancers & career switchers: Turn AI into client work or a new career.
AI engineering course curriculum — 12 weeks
Phase 1 (Weeks 1–4): LLM Foundations, Claude Code & RAG
Week 1 — Discover, Frame & Model Basics
- FDE operating model and lifecycle
- Discovery interviews and process mapping
- AI vs ML vs rules
- Success metrics and baselines
- How models are trained and why they hallucinate
- Model landscape and selection
- Lab: Guided Code Walkthrough
Week 2 — Model Behaviour, Prompting & Context
- System prompts and persona design
- Structured outputs and schema enforcement
- Reasoning models and thinking budgets
- Prompt caching and batching
- Grounding and citations
- LLM vs classical-baseline benchmark
Week 3 — Build with Claude Code & Data Foundations
- Claude Code workflow (CLAUDE.md, plan, implement, test)
- Extending Claude Code (skills, subagents, hooks, plugins)
- Full-stack app (UI, API, auth)
- Git and diff review of AI-written code
- SQL, schema design, data modelling
- PII classification and DPDP basics
Week 4 — Knowledge & Retrieval
- Parsing and ingestion (PDF, OCR, tables)
- Chunking and embeddings
- Vector stores and ANN
- Hybrid search and reranking
- Citations and grounded answers
- Query rewriting and agentic RAG
- Lab: Find Where the AI Application Failed (1 of 2)
Phase 2 (Weeks 5–8): AI Agents, MCP, Evals & AI Security
Week 5 — Agent Architecture & Memory
- Tool design and schemas
- Agent loop and bounded autonomy
- Human-in-the-loop approvals
- Building an MCP server (2026-07-28 stateless spec)
- Short-term and long-term memory
- Durable workflows
- Lab: Find Where the AI Application Failed (2 of 2)
Week 6 — Multi-Agent & Enterprise Integration
- Multi-agent patterns (router, supervisor, parallel, handoff)
- Subagents and the Claude Agent SDK
- Channels (WhatsApp, email)
- REST/GraphQL, webhooks, idempotency
- Workflow orchestration and queues (n8n)
- Rate limits, backoff, dead-letter handling
Week 7 — Evaluation & Quality
- Eval design and failure taxonomy
- Golden sets with client SMEs
- LLM-as-judge (rubric, pairwise, calibration)
- RAG and agent evals
- CI regression gates
- Reporting quality to a client
Week 8 — Safety, Security & Governance
- Threat modelling (OWASP LLM Top 10)
- Prompt injection, direct and indirect
- Red-team method and war room
- Guardrails and PII redaction
- Identity and permissions (RBAC)
- Data residency, retention and DPDP
Phase 3 (Weeks 9–12): Deploy, Prove ROI & Capstone
Week 9 — Deploy & Operate
- Docker and CI/CD
- Cloud deployment with IAM and networking basics
- Release safety (canary, A/B, shadow, rollback)
- Observability (logs, traces, token and latency metrics)
- SLOs, error budgets, alerting
- Incident response and postmortems
Week 10 — Economics, Delivery & Ownership
- Model selection matrix and routing
- Unit economics per request
- ROI proof and business case
- SOW, commercials, pricing negotiation
- Executive readout
- Handover and enablement
Week 11 — Capstone: Discover & Design
- Select a capstone brief (or bring your own client)
- Run a 48-hour discovery window
- Write the problem brief and success measures
- Architecture with architecture decision records
- Threat model and risk register
- First working slice
Week 12 — Capstone: Build, Harden & Demo Day
- Build against the hidden scenario suite
- Run the eval harness and fix the top failures
- Deploy with tracing and cost controls
- The 15-minute demo
- The live defence against an unseen change request
- The 12 graduation questions, answered live
AI engineering course guide
What is an AI engineering course?
An AI engineering course teaches you to build software on top of large language models (LLMs), not to train models from scratch. This one covers generative AI, prompt and context engineering, retrieval-augmented generation (RAG), AI agents and MCP servers, then takes every build to production with evaluation, security and LLMOps.
Skills you will learn
- Generative AI
- Large language models (LLMs)
- Prompt & context engineering
- Structured outputs
- RAG pipelines
- Embeddings & vector databases
- Hybrid search & reranking
- AI agents
- Agentic AI
- Multi-agent systems
- MCP servers
- Claude API
- Claude Code
- Claude Agent SDK
- LLM evaluation
- AI security & guardrails
- LLMOps & deployment
- Full-stack AI apps
- SQL & data modelling
- DPDP & PII basics
Your AI engineer roadmap, in 12 weeks
Weeks 1–4: LLM foundations, Claude Code and RAG. Weeks 5–8: AI agents, multi-agent systems, MCP, evals and AI security. Weeks 9–12: deployment, ROI and a client capstone ending in Demo Day. The 2026 AI engineer roadmap, done as live builds instead of videos.
For working professionals, freshers and career switchers
Live weekend classes, about 12 hours a week including build time, so you can take this AI engineering course alongside a full-time job or your final year of college. Basic coding helps; a guided code walkthrough in Week 1 brings newer coders up to speed.
Certification, portfolio and placement support
Earn the Menler AI FDE Certification, graded on 100 engineering and delivery competencies, with six deployed AI projects in your portfolio. Placement support includes mock FDE interviews, portfolio and LinkedIn reviews, and referrals through Menler's hiring network.
Live online, anywhere in India
Join from Bengaluru, Hyderabad, Pune, Chennai, Mumbai, Delhi NCR, Kolkata, Kochi or anywhere else in India. Every session is live online, in pods of five with mentors, and every recording stays in the LMS for a year.
AI Engineer vs Forward Deployed Engineer vs ML Engineer
| AI Engineer | Forward Deployed Engineer | ML Engineer | |
|---|---|---|---|
| What they build | LLM apps, RAG systems and AI agents | The same, deployed inside a client’s business | Trained models and data pipelines |
| Measured on | The system and its code | The client outcome and its ROI | Model accuracy |
| Core skills | Prompting, RAG, agents, evals | All of that, plus discovery, deployment and adoption | Statistics, model training, MLOps |
| In this course | Yes, all of it | Yes, the heart of the course | The fundamentals, in Week 1 |
Frequently asked questions
What is the AI FDE Fellowship?
The AI FDE Fellowship is Menler's 12-week, live online AI engineering course for becoming an AI Forward Deployed Engineer. You build RAG systems, AI agents and MCP servers with Claude on six real Indian business cases, deploy them to production, and earn the Menler AI FDE Certification.
What is a Forward Deployed Engineer (FDE)?
A Forward Deployed Engineer is an engineer who embeds with a client, understands their problem, builds the AI system and owns it in production. The role combines software engineering with the judgment of a consultant.
What is the difference between an AI engineer and a Forward Deployed Engineer?
An AI engineer is measured mainly on the system and its code. A Forward Deployed Engineer is measured on the client outcome, so they also own discovery, deployment, adoption and the business case. The AI FDE Fellowship trains both.
How do I become a Forward Deployed Engineer?
To become a Forward Deployed Engineer, learn to frame a client problem, build the AI system (RAG, agents, integrations), deploy it and prove its return, then show deployed work. The AI FDE Fellowship follows that path in 12 weeks and ends with six deployed builds, six Case Defences and Demo Day.
Who is this AI engineering course for?
The AI FDE Fellowship is for students and fresh graduates, engineers and developers, product, design and delivery professionals, founders and domain experts, and freelancers or career switchers who want to build and ship AI systems.
Do I need to know how to code?
Basic programming helps but is not required. If you're newer to code, the Builder track uses Claude Code and a guided code walkthrough in Week 1 to bring you up to speed.
When does the next batch start?
The next batch of the AI FDE Fellowship starts on November 14, 2026. It runs live online on weekends for 12 weeks.
Is the AI engineering course online?
Yes. The AI FDE Fellowship is fully live and online: 24 weekend sessions, 48 live hours over 12 weeks, in pods of five learners with mentors, plus recordings and one year of access to the LMS and community.
How much time should I commit each week?
About 12 hours a week: four live hours on weekends plus around 7–8 hours of build time.
Can I take this AI engineering course while working full time?
Yes. Classes are live on Saturdays and Sundays, and the rest is about 7–8 hours of build time you schedule yourself, so the AI FDE Fellowship fits alongside a full-time job.
Can freshers or final-year students join?
Yes. Students and fresh graduates are one of the groups the AI FDE Fellowship is built for. You finish with six deployed AI projects, which gives a fresher a portfolio most applicants do not have.
Is this an AI engineering bootcamp?
It works like one: an intensive, live, cohort-based 12-week programme where you build and ship every week. Unlike most bootcamps, every build is a real Indian business case, and each one is defended to a panel.
How is this different from a machine learning or data science course?
A machine learning or data science course teaches you to train models from data. This AI engineering course teaches you to build products on top of large language models — RAG, AI agents and MCP servers — and to deploy them. Model training is covered only as background in Week 1.
What tools and technologies will I learn?
Claude is the core stack: the Claude API, Claude Code, the Claude Agent SDK and MCP. You also work with vector databases, hybrid search and reranking, evaluation tools, SQL, Git, integration platforms and production deployment (LLMOps).
Is the AI FDE Fellowship only about Claude?
Claude is the primary stack, but not the only one. You also compare models, use vector databases, eval tools and integration platforms, and learn model routing. You use your own Claude API key, so every build stays in your own account.
What will I build during the Fellowship?
Six builds across six real cases: a bank grievance triage engine, a cited farm-advisory RAG copilot, a GST reconciliation agent behind a public MCP server, a certified health pre-authorisation assistant, a production failed-delivery recovery system, and a client capstone you choose.
What is RAG, and will I build RAG systems?
RAG (retrieval-augmented generation) lets an AI answer from your own documents, with citations, instead of from memory. Yes: you build RAG pipelines from Week 4, covering parsing, chunking, embeddings, vector stores, hybrid search, reranking and grounded answers.
What is MCP (Model Context Protocol)?
MCP is an open standard that lets an AI model use outside tools and data — your database, files or business apps — through one common interface. In the AI FDE Fellowship you build and publish your own MCP server, used by a GST reconciliation agent.
What is agentic AI?
Agentic AI is AI that plans and carries out multi-step tasks — calling tools, checking results and deciding what to do next — instead of answering one prompt. Weeks 5–8 of the course cover agent architecture, memory, multi-agent systems and keeping agents safe.
Will I get a certificate?
Yes. You earn the Menler AI FDE Certification, graded against 100 engineering and delivery competencies.
Does the course include placement support?
Yes. Placement support is part of the AI FDE Fellowship: mock FDE interviews, portfolio and LinkedIn reviews, hiring-readiness guidance and referrals through Menler's hiring network.
How does interview preparation and career support work?
Career support runs through the whole Fellowship: mock FDE interview loops, portfolio and LinkedIn reviews, hiring-readiness guidance, and referrals through Menler's hiring network.
What is the program fee and are there payment options?
The 12-week AI FDE Fellowship costs ₹75,000 including all taxes, with EMI from ₹4,999 a month. Scholarship options are shared during the application process and counselling session.
What is the refund and cancellation policy?
Refunds are only applicable if program access has not been provided. Once access to the Fellowship platform, curriculum, or learning resources has been granted, fees become non-refundable and cancellations are not eligible for a refund.