Quick answer: this forward deployed engineer (FDE) course takes working engineers from “I can call a model” to “I can deploy an AI system inside a business and get it adopted”. It runs over seven modules and a capstone built on your own work: discovery, architecture, RAG, AI agents, evaluation, production and adoption. Take it as 1-to-1 coaching or as a cohort for your team, live online or onsite in India and the UAE.
Still deciding? See the full syllabus, how it compares or book a free call.
Teams at these organisations have trained with Hitesh Motwani and Skillopedia
200,000+ professionals · 16+ countries · 4.8★ average session rating · Corporate clients include Google, Microsoft & Tata Group
Everything people ask before they enquire: who it suits, what you need to know already, how it runs and what you walk away with.
| Course detail | What you get |
|---|---|
| Who it is for | Working software, data, ML and solutions engineers, tech leads, and teams whose engineers are moving into client-facing AI delivery |
| Prerequisites | Working Python (or another general-purpose language), REST APIs and Git. Basic SQL helps. No prior LLM, RAG or agent experience needed |
| Syllabus | Seven modules: GenAI foundations, enterprise architecture, AI agents, RAG, real AI products, the deployment framework and frontier platforms |
| Capstone | A deployed RAG or agent service for a real brief from your own work, with a discovery document, evaluation set and 90-day adoption plan |
| Formats | Half-day primer, full-day build bootcamp, multi-week cohort, custom corporate programme, or 1-to-1 coaching |
| Delivery | Live online worldwide; onsite at your office in India or the UAE, with live online labs between onsite days |
| Group size | 15–25 engineers per team cohort; 1-to-1 for individuals |
| Certificate | Skillopedia Forward Deployed Engineer certificate for a panel-reviewed capstone. A training certificate, not a vendor credential |
| Fees | Quoted per engagement after a short scoping call; team cohorts usually on a day-rate model, 1-to-1 coaching quoted per package |
| Language | English and Hindi |
| Placement | None. This is skills training; no job, placement or salary outcome is promised |
What makes it different: every lab and the capstone use a problem from your own job, so what you build keeps working after the course. If you want the wider picture of the role first, read the 90-day forward deployed engineer roadmap.
The FDE seat mixes engineering, consulting and ownership. The course suits people who already build software and now need to make AI work inside a real business.
The course assumes you can already build software. It does not assume any AI background. A short skills check before kick-off sets the starting depth.
| Skill | Needed on day one | Built during the course |
|---|---|---|
| Programming | Working Python or a similar language: functions, packages, reading other people’s code | Async calls, streaming, structured outputs, testing AI code |
| APIs and data | Calling a REST API, reading JSON, basic SQL | Connecting models to internal systems through tools and MCP |
| Version control | Git basics: branch, commit, pull request | Reviewing AI-generated code safely |
| Cloud | Know what a container, a secret and a log are | Deploying, monitoring and costing an AI service on your cloud |
| AI | Nothing beyond curiosity | LLMs, RAG, agents, evaluation, guardrails |
| People skills | Willingness to ask a business user awkward questions | Structured discovery, scoping and adoption planning |
Quick self-check: if you can write a small script that calls an API, filters the result and saves it, and you have a real problem with sample data to work on, you are ready.
Share a line about your experience (or your team), the stack you use and the system you would like to deploy. You will get the module plan at the right depth, a suggested capstone brief and a quote. Prefer to talk first? Book a free call.
Each module pairs a short explanation with a lab on your own work. The order below is the reference path; depth and sequence are tuned to your skills check and stack.
How large language models behave and fail, the current model families from Anthropic, OpenAI, Google and open-weight providers, structured prompting, and why every prompt needs a test set before it ships.
You build: a small evaluation harness and a map of where AI could help in your own business unit.
Fitting AI into real company systems: APIs, data pipelines, identity and permissions, cloud services and the security questions an IT team will ask. A simple framework for choosing between RAG, agents, fine-tuning and plain code.
You build: a one-page architecture for your capstone, reviewed in the session.
Tool calling, orchestration, human approval steps and multi-agent workflows. Connecting models to internal tools through MCP and connectors, and deciding when an agent is the wrong answer.
You build: an agent that completes one real task from your workflow with a person signing off.
Chunking, embeddings, vector and hybrid search, re-ranking, citations and document-level access control, plus how to tell whether retrieval or generation is causing a bad answer.
You build: a grounded assistant over your own documents, with a retrieval test set.
Turning a working notebook into something people use every day: streaming, authentication, rate limits, caching, model routing and a usable interface. Patterns for support, sales, finance, HR and meeting workflows.
You build: a small internal tool your colleagues can log into and try.
Discovery workshops with stakeholders, scoping and pushing back, observability, guardrails, red-teaming basics, cost control, and the change management that turns a launch into a habit.
You build: a discovery document, a monitoring view and a draft 90-day adoption plan.
Comparing the Claude, OpenAI and Gemini APIs and their agent tooling, AI-assisted coding with tools such as Claude Code, Cursor and GitHub Copilot, and judging vendor features against your requirements.
You build: a short vendor comparison for your use case that a manager can act on.
One end-to-end delivery: brief, discovery, build, evaluation, deployment and adoption plan, presented to a review panel. It pulls every module together on a problem you chose.
You build: a deployed service and the documents an FDE hands to a business owner.
The capstone is where the course stops being a course. You take a problem that matters to you or your employer and deliver it the way an FDE would, from the first conversation to the adoption plan.
| Stage | What you do | What the panel looks for |
|---|---|---|
| 1. Brief | Pick a real problem from your job or company and write it down in the business owner’s words | A problem someone actually has, with a person who would use the fix |
| 2. Discovery | Interview the owner and users, map the current process and agree what “better” means | A scoped problem and a measurable target, not a feature list |
| 3. Design | Choose the pattern (RAG, agent, workflow or plain code) and sketch the architecture | A defensible choice, with data access and security thought through |
| 4. Build | Implement the smallest version that solves the problem end to end | Working code that runs on your stack, not only in a notebook |
| 5. Evaluate | Build a test set from real examples and measure accuracy, refusals and cost | Honest numbers, and fixes for the worst failure modes |
| 6. Deploy | Ship it with logging, monitoring and guardrails | Something a colleague can use without you sitting beside them |
| 7. Adopt | Write a 90-day adoption plan and present it to the panel | A named owner, training for users and a way to see whether it is used |
“Our customer-success team spends most of Monday answering the same contract and billing questions from three systems.” A participant interviews the team, finds that four question types cover most of the volume, and builds a retrieval assistant over the contract library with a read-only billing lookup. A test set of past questions shows where it fails; refunds are routed to a person. The adoption plan names the team lead as owner.
That example is illustrative, not a promised result. Where company data is sensitive, labs use masked or synthetic copies under your NDA, and the code stays yours or your employer’s.
There are no fixed public batch dates for this course. It runs when you or your team are ready, at a pace that fits real work, and the length depends on the format you choose.
| Format | Typical length | Best for | Where |
|---|---|---|---|
| 1-to-1 coaching | A series of online sessions, paced around your working hours | An individual engineer working towards FDE-style work, with reviews of their own builds | Live online |
| Half-day primer | 3–4 hours | Engineers and leads deciding what FDE work would mean for them | Onsite or live online |
| Full-day build bootcamp | 6–7 hours | A team going from idea to a working prototype on its own data | Onsite preferred |
| Multi-week cohort | Several sessions over a number of weeks | The complete course: seven modules, capstone and certificate | Onsite kick-off, live online labs |
| Custom corporate programme | Agreed with you | Several cohorts or locations, rebuilt around your engagement types | Your offices in India or the UAE, or online |
Fees: the course is quoted per engagement, because format, group size, number of sessions, lab environment and travel all change the work. Team cohorts usually follow a day-rate model with a 50% advance on confirmation; 1-to-1 coaching is quoted as a package. You see a written proposal before you commit. Individuals: you can also join a cohort your employer runs, so it is worth asking your L&D team first.
Yes. Engineers who complete the capstone and pass the panel review receive the Skillopedia Forward Deployed Engineer certificate. Be clear about what that is: a training certificate that records a reviewed, deployed project. It is not an Anthropic, AWS or Palantir credential, and no employer requires a particular FDE certificate.
| Credential | From | How you get it |
|---|---|---|
| Skillopedia Forward Deployed Engineer certificate | This course | Complete the capstone and pass the panel review |
| Claude Frontier Academy badges | Anthropic | Employer nomination, a graded practical and a residency; not an open exam |
| FDE validated certification and advanced credential | AWS | Free learning pathways on AWS Skill Builder |
| Certified Forward Deployed Engineer (CFDE) | ADaSci | An exam-based, third-party credential |
Hiring managers judge FDE candidates mainly on what they have shipped and how they talk about it. That is why the capstone produces a write-up you can discuss in an interview: the problem, the architecture, the evaluation numbers and what changed for users. For a detailed, sourced look at vendor programmes, see the FDE certification guide on Hitesh’s FDE trainer page.
There is no single right FDE course. The best choice depends on whether you need a credential, a career change or the ability to deploy AI in the job you already have.
| Option | Example (checked 26 Oct 2026) | Length and format | Strength | Watch for |
|---|---|---|---|---|
| Self-paced online course | FDE courses on Educative and Udemy | Hours to weeks, at your own pace | Cheap, flexible, good for concepts | Nobody reviews your work; no real stakeholders |
| University-linked certificate | IIT Delhi CEP Advanced Certificate in AI Forward Deployed Engineering | 6 months, 145 hours, weekend live online; listed at ₹1,65,000 + GST | An institution’s certificate and a structured syllabus | How much of the work is on your own systems |
| Long online career programme | Scaler AI Forward Deployed Engineer Program | 12 months, live and mentor-led; listed at ₹3,99,000 | Breadth, mentoring, career services | Time commitment and how career claims are worded |
| Immersive cohort | Masai Leap AI FDE | 4 days in Goa, 10 weeks online and a Bengaluru hackathon; ₹1.5 lakh + GST | Intense, peer-driven | Fixed dates and travel |
| Vendor pathway | AWS Skill Builder FDE pathways; Anthropic Claude Frontier Academy | Self-paced (AWS); employer-nominated residency (Anthropic) | Deep on one vendor’s platform | Eligibility, and less on discovery and adoption |
| This course | Hitesh Motwani, 1-to-1 or team cohort | Half-day to multi-week, live online or onsite; quoted per engagement | Built on your own work and stack, with in-session reviews and a panel capstone | No public batches and no placement service |
Third-party details are taken from the providers’ own pages and change often. Confirm dates and fees with each provider before you enrol. More options are compared in AI bootcamps in India.
Forward deployed engineering grew out of Palantir’s practice of sending engineers to work inside customer organisations. Generative AI turned it into one of the most talked-about roles in tech: AI companies, cloud providers, IT services firms and global capability centres all need engineers who can make models work inside messy, real processes. Most openings still ask for several years of engineering experience, so treat the role as a direction you grow into rather than a job a course hands you.
A sensible path is to strengthen your engineering base, ship two or three small AI systems that real people use, and practise the client side of the job: discovery, scoping, explaining trade-offs and driving adoption. The forward deployed engineer roadmap lays that out as a 90-day self-study plan, and the course is the guided, reviewed version of the same journey. For a view of how FDE pay compares with other roles, see the highest-paying AI jobs in India. No course can promise a particular job or salary, and this one does not.
Hitesh Motwani, Mumbai and Dubai
Hitesh’s training has one rule: people practise on their own work. In this course that means your repository, your documents and your stakeholders, with architectures, prompts and evaluation results reviewed in the session rather than weeks later. You leave with a system that runs and habits you use the next morning, and because each deployment teaches the next one, the time saved compounds.
“Hitesh’s depth of understanding on how the generative AI space is changing and how to write perfect prompts was awesome.”
Hands-on AI build session, India
Corporate training, CapitaLand, Bengaluru
CMO workshop, IIM Bangalore
Hitesh Motwani in a training session
Four steps, usually completed within a couple of weeks for individuals; team cohorts depend on your calendar.
Tell us about your experience or your team, your stack and what you would like to deploy. A fifteen-minute call is enough.
A short practical check, so the course starts where you actually are instead of at slide one.
Module plan, format, capstone brief, schedule and fees, for you or your manager to sign off.
Environment set up on your stack, the first discovery exercise, and your first lab before the end of the week.
The questions engineers and engineering managers ask most before they sign up. Have another one? Send it and you will get a straight answer.
It is training for the forward deployed engineer role: building and deploying AI inside a real business and getting people to use it. A good FDE course covers discovery with stakeholders, architecture, RAG, AI agents, evaluation, production deployment and adoption, and ends with a capstone you actually ship.
Seven modules: generative AI for engineers, enterprise AI architecture, AI agents and automation, RAG and knowledge systems, building real AI products, the FDE deployment framework, and working with frontier AI platforms, followed by a capstone on your own work. See the syllabus.
Working software, data, ML and solutions engineers who want applied AI work, consultants who own AI engagements, and engineering teams that need to take AI from pilot to production. It is not designed for people who are new to programming.
Working Python or a similar language, REST APIs and Git. Basic SQL and cloud awareness help. You do not need prior experience with LLMs, RAG or agents; the course builds that.
It depends on the format: a half-day primer is 3–4 hours, a full-day bootcamp 6–7 hours, and the complete course with capstone runs as a multi-week cohort or a series of 1-to-1 sessions. The exact schedule is set in your proposal.
Yes. It runs live online worldwide, and 1-to-1 coaching is always online. Team cohorts can also be delivered onsite at your office in India or the UAE, with live online labs between onsite days.
Both. Individuals take the course as 1-to-1 coaching built around their own project, or join a cohort their employer runs. Companies book cohorts of 15–25 engineers. There are no fixed public batch dates.
Yes. Complete the capstone and pass the panel review to receive the Skillopedia Forward Deployed Engineer certificate. It is a training certificate, not a vendor credential from Anthropic, AWS or Palantir. See certificates.
Fees are quoted per engagement because format, group size, sessions and travel change the work. Team cohorts usually follow a day-rate model; 1-to-1 coaching is quoted as a package. Send the form for a written quote.
No. It is skills training. You leave with a deployed project, an evaluation report and a write-up you can discuss in interviews, but no job, placement or salary outcome is promised. Be cautious of any course that guarantees one.
It is possible but uncommon, because most FDE roles ask for experience. Freshers usually do better building programming fundamentals first, then moving into implementation or solutions roles. The FDE roadmap sets out a path.
Not as an open exam. Anthropic’s Claude Frontier Academy, announced in October 2026, admits engineers nominated by organisations working with Anthropic. AWS offers FDE pathways on Skill Builder, and ADaSci offers an exam-based CFDE credential.
A grounded assistant over your own documents, an agent that completes a real task with human sign-off, an evaluation harness, a small internal tool, and a capstone service with its discovery document and 90-day adoption plan.
Hitesh Motwani, a corporate AI trainer and author of Generative AI 360°, with 17+ years in training. He has trained 200,000+ professionals across 16+ countries, and his corporate clients include Google, Microsoft and Tata Group. More on the about page.
Guides, programmes and city pages that go deeper on the FDE role.
Bring the problem you would most like to solve with AI, the stack you work in and how much time you have. You will get a course plan at the right depth and a capstone brief built on your own work.
1-to-1 coaching online · Team cohorts onsite in India and the UAE or live online · English and Hindi
Choose a slot that suits you. The call is free, takes about fifteen minutes and there is no obligation.
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Tell us whether the course is for you or for a team, how much engineering experience is involved and what you want to ship. You will get a written outline, the format that fits and a quote.