Forward Deployed Engineer Course: Become an AI FDE
For procurement, supply-chain and operations people moving into the role, with no coding background needed. You work through one simulated client engagement and finish with a portfolio case study.
30 chapters, 96 lessons
14-day refund on the yearly plan. Real pricing on the plans page, no surprises.
96
lessons, yours to run
Easy to pick up, built to get you the next job
No coding background needed, and you write code with Claude as you go.
Every section below turns into something a hiring manager recognises, not just notes.
Read any client's real problem and know exactly what to build
- You'll learn the FDE method for getting inside a client's actual workflows, not the tidy version they show on slide decks
- You'll run a continuous simulated engagement from day one, so every technique lands on a real scenario rather than a blank exercise
- No coding background needed: the course is built for people who understand business problems, and teaches the technical side in plain steps from lesson one
- You'll be able to explain in an interview exactly how an FDE differs from a consultant, a solutions engineer, and an ML engineer, the distinction hiring managers test for
Build and ship working AI tools inside a client's environment
- You'll use Claude Code and MCP to build working agents against a client's data, not toy demos
- You'll practise the FDE core loop: observe the workflow, identify the blocker, build a targeted artifact, ship it the same day
- Every tool you build is portfolio-ready evidence a hiring manager can see running, not a slide summarising what you learned
- Hands-on from lesson one, with mentor review so you are never stuck alone on a technical step
Hold the client relationship while you build, the part most engineers never learn
- You'll practise the plain-English stakeholder update: one sentence that keeps a non-technical sponsor on side without a status-report deck
- You'll learn how to chase data access and unblock yourself without burning the relationship
- You'll run structured discovery sessions so you build the right thing first rather than rebuilding after a demo
- These are the client-facing capabilities a hiring manager recognises immediately as FDE-ready, not generic soft skills
Package your work and land the role
- You'll assemble a portfolio from your engagement artifacts that shows the full line from problem to running software
- You'll practise the interview answer that explains the FDE origin story in your own words, the answer that separates confident candidates from vague ones
- You'll leave with a repeatable method for reading any new industry fast, so your next engagement does not start from zero
- The result is a profile that stands out to employers looking for the rare operator who can build and communicate
Finish this course and you can do all of this, no prior background required:
- You'll be able to read a job posting and identify with precision whether it is a genuine forward-deployed role, the first filter every serious FDE candidate needs
- You'll be able to enter any client environment, map their real workflows and data, and identify the build that delivers the most value, the core skill hiring managers in AI deployment pay for
- You'll be able to build and ship working AI tools using Claude Code against a client's actual data, with no prior coding background, giving you the technical credibility the role demands
- You'll be able to hold a client relationship while you build: running discovery, communicating progress in plain English, and unblocking yourself on data access without burning trust
- You'll be able to present a portfolio case study showing one complete engagement from first observation to shipped, running software, the kind of evidence that makes a hiring manager call the same day
- You'll be able to use a repeatable industry-reading method on any new engagement, so you ramp fast regardless of the sector the client is in
96 lessons, 30 chapters
Built from real implementation work
Every agent, skill file, and dataset is drawn from 25+ years and 50+ Oracle ERP implementations across pharma, manufacturing, semiconductor, and distribution.
Kept current with the stack
Claude Code, skills, and MCP move fast. Your licence includes every system update for as long as you're subscribed, so what you build keeps working as the tools change.
Forward deployed engineer job ads want technical skills and client skills together, and they filter out anyone who can only show one of them. If you come from procurement or supply chain, you already understand messy client workflows. What you can't show yet is a GitHub repo or an AI tool you built, so the application goes nowhere. The title doesn't help either. Some postings say solutions engineer, some say deployment engineer, and without a clear picture of the job it's easy to apply for the wrong ones.
What you'll be able to do, module by module
Every module leaves you able to build and run something real. Here is the syllabus, in the order you work through it.
Curriculum
30 chapters · 96 lessons
Orientation
You can explain in your own words why the FDE role was invented, where the lines are between an FDE, a consultant, a solutions engineer, and an ML engineer, and you can read a real job posting and identify whether the role is genuinely forward-deployed, giving you a reliable filter before you apply
0.1 What Is an FDE, Really?4 items
- 0.1.1 The origin of the FDE role and why companies created it🔒
- 0.1.2 FDE vs. ML Engineer vs. Solutions Engineer vs. Consultant, where the lines actually are🔒
- 0.1.3 A real day-in-the-life walkthrough, hour by hour🔒
- 0.1.4 Why 'generic AI enthusiasm' is not the same skill, and what the real gap is🔒
0.2 Meet the Case Study: Meridian Logistics Needs Help4 items
- 0.2.1 The client's stated problem (what they think they need)🔒
- 0.2.2 Company background, size, industry context, and why framing matters🔒
- 0.2.3 The stakeholders you'll deal with across the course🔒
- 0.2.4 What 'success' looks like from the client's point of view🔒
0.3 The 4-Week Roadmap: What You'll Be Able to Do by Week 43 items
- 0.3.1 Skill map: what is added each week and why in that order🔒
- 0.3.2 The 4 portfolio artifacts you'll walk away with🔒
- 0.3.3 How much time to budget per week (realistic hours)🔒
0.4 Setting Up Your Toolkit4 items
- 0.4.1 Accounts and access you'll need (Claude, an IDE, relevant APIs)🔒
- 0.4.2 Installing and verifying your environment🔒
- 0.4.3 A first 'hello world' test confirming everything works before Week 1🔒
- 0.4.4 Troubleshooting common setup failures🔒
Week 1: Foundations, Any Domain, Any Industry
You can enter an unfamiliar client industry, map its workflows and data structures quickly, and identify the highest-value problem worth building against, so you are never starting from zero on a new engagement
1.1 The FDE's Real Job: Translator Between Business Problems and AI Capability3 items
- 1.1.1 Why business problems rarely arrive in AI-shaped language🔒
- 1.1.2 The translation loop: listen, reframe, propose, validate🔒
- 1.1.3 A real translated example: messy client ask to concrete technical scope🔒
1.2 How to Read Any Domain, Not Just Learn One4 items
- 1.2.1 The 5-question framework for entering an unfamiliar industry🔒
- 1.2.2 Where to find domain knowledge fast (docs, SMEs, existing workflows)🔒
- 1.2.3 Practice pass: applying the framework to an industry you've never touched🔒
- 1.2.4 Common trap: assuming your last domain's rules apply everywhere🔒
1.3 Anatomy of a Client Problem: Symptoms vs. Root Cause vs. What AI Can Actually Fix3 items
- 1.3.1 Separating what a client complains about from what is actually broken🔒
- 1.3.2 A root-cause drilldown method (the '5 whys' applied to AI scoping)🔒
- 1.3.3 Knowing what AI genuinely cannot fix, and saying so early🔒
1.4 Your First Tool: Prompting as a Discovery Instrument3 items
- 1.4.1 Prompting to interrogate a problem, not just generate output🔒
- 1.4.2 Structuring a discovery prompt for an unfamiliar dataset or workflow🔒
- 1.4.3 Iterating a prompt based on what it reveals, not just what it produces🔒
1.5 Case Study Checkpoint: Diagnosing Meridian's Real Problem4 items
- 1.5.1 Reviewing Meridian's stated request against the 1.3 framework🔒
- 1.5.2 Uncovering the actual root cause🔒
- 1.5.3 Writing the revised, accurate problem statement🔒
- Checkpoint: Would a client trust you to diagnose this alone?Quiz
1.6 Week 1 Portfolio Artifact: Your Domain-Agnostic Problem Framework3 items
- 1.6.1 Turning the 1.2 and 1.3 frameworks into a reusable one-page template🔒
- 1.6.2 Filling it out live for Meridian as your first proof sample🔒
- 1.6.3 Checklist: what makes this artifact interview-ready🔒
Week 2: Core Technical Skills, Applied Regardless of Domain
You can use Claude Code and MCP to build working AI agents and tools against a client's real data and workflows, following a structured build loop that produces deployable artifacts without requiring a prior coding background
2.1 Prompt Engineering for Client Work4 items
- 2.1.1 The difference between a toy prompt and a production prompt🔒
- 2.1.2 Structuring prompts for reliability, not one good output🔒
- 2.1.3 Handling edge cases and ambiguous inputs in prompt design🔒
- 2.1.4 Testing a prompt against multiple real-world variants🔒
2.2 Tool Use and MCP: Giving AI Access to Real Systems4 items
- 2.2.1 What MCP actually is, in plain terms, and why it matters to an FDE🔒
- 2.2.2 Connecting a model to a real data source or API🔒
- 2.2.3 Hands-on build: wiring up one working tool call🔒
- 2.2.4 Troubleshooting common connection and permission failures🔒
2.3 Agent Basics: When to Use One, When It's Overkill3 items
- 2.3.1 What actually makes something an 'agent' versus a scripted pipeline🔒
- 2.3.2 A decision checklist: does this problem need an agent at all🔒
- 2.3.3 Building a minimal single-step agent as a first example🔒
2.4 Working with Messy, Real Data3 items
- 2.4.1 What 'messy' looks like across different industries🔒
- 2.4.2 A repeatable cleanup and validation pass before feeding data to a model🔒
- 2.4.3 Handling missing, contradictory, or mislabeled data gracefully🔒
2.5 Case Study Checkpoint: Building Meridian's First Working Prototype4 items
- 2.5.1 Scoping the smallest version that proves the concept🔒
- 2.5.2 Building it step by step with the tools from 2.1 to 2.4🔒
- 2.5.3 Testing it against a realistic (simplified) version of Meridian's data🔒
- Checkpoint: Would a client trust you to build the first prototype alone?Quiz
2.6 Side Quest: Rebuild the Same Prototype for a Second, Different Industry3 items
- 2.6.1 Picking a second, unrelated domain to test transferability🔒
- 2.6.2 Adapting the prototype with minimal changes🔒
- 2.6.3 Reflection: what had to change, what stayed the same🔒
2.7 Week 2 Portfolio Artifact: A Working Agent/Tool Demo3 items
- 2.7.1 Packaging the Meridian prototype into a shareable demo🔒
- 2.7.2 Writing a short technical README explaining what it does and why🔒
- 2.7.3 Checklist: what makes this demo credible to a technical reviewer🔒
Week 3: Client-Facing Skills, the Part Nobody Teaches
You can run a structured discovery session, keep a non-technical sponsor informed in plain language, chase data access without damaging the relationship, and course-correct your build before a demo exposes a wrong assumption
3.1 Debugging Live, In Front of a Client3 items
- 3.1.1 The mental shift from solo debugging to debugging under observation🔒
- 3.1.2 A calm, structured live-debugging process for use under pressure🔒
- 3.1.3 What to say when you genuinely do not know the answer yet🔒
3.2 Translating Technical Tradeoffs into Business Language3 items
- 3.2.1 Common tradeoffs FDEs face (cost, speed, accuracy, maintainability)🔒
- 3.2.2 Reframing each tradeoff in terms a non-technical stakeholder cares about🔒
- 3.2.3 Practice: rewriting a technical tradeoff for a business audience🔒
3.3 Handling Ambiguous or Shifting Requirements3 items
- 3.3.1 Why requirements shift constantly in embedded engagements🔒
- 3.3.2 A lightweight re-scoping method that preserves client trust🔒
- 3.3.3 Documenting scope changes so nobody is surprised later🔒
3.4 Reading the Room: When to Push Back vs. When to Just Build It3 items
- 3.4.1 Signals that a pushback conversation is worth having🔒
- 3.4.2 Disagreeing with a client without damaging the relationship🔒
- 3.4.3 Knowing when to build it anyway, even if you'd have done it differently🔒
3.5 Case Study Checkpoint: Meridian's Requirements Just Changed4 items
- 3.5.1 The new requirement Meridian introduces mid-engagement🔒
- 3.5.2 Re-scoping the prototype using the 3.3 method🔒
- 3.5.3 Communicating the change back to the (simulated) client🔒
- Checkpoint: Would a client trust you when the requirements change?Quiz
3.6 Presenting Progress Without Losing the Room3 items
- 3.6.1 Structuring an update for mixed technical and non-technical audiences🔒
- 3.6.2 A working demo script template🔒
- 3.6.3 Handling tough questions live during a demo🔒
3.7 Week 3 Portfolio Artifact: A Stakeholder-Ready Progress Deck3 items
- 3.7.1 Building the deck using the 3.6 script structure🔒
- 3.7.2 Including the 3.5 scope-change story as a credibility signal🔒
- 3.7.3 Checklist: what makes a deck client-ready, not just internally clear🔒
Week 4: Shipping, Packaging, and Getting Hired
You can package your engagement artifacts into a portfolio that shows the full line from client problem to running software, answer the defining FDE interview questions with the origin-story depth that separates confident candidates, and apply a repeatable industry-reading method to any future engag
4.1 Finishing an Embedded Engagement3 items
- 4.1.1 What proper handoff includes (docs, access, support plan)🔒
- 4.1.2 Getting explicit client sign-off, and why skipping it backfires🔒
- 4.1.3 Common loose ends beginners forget to close🔒
4.2 Case Study Checkpoint: Meridian Logistics, Final Delivery4 items
- 4.2.1 Assembling everything from Weeks 1 to 3 into one final delivery🔒
- 4.2.2 Final validation against the original AND revised problem statements🔒
- 4.2.3 Writing the final handoff summary🔒
- Checkpoint: Would a client trust you to ship and hand off alone?Quiz
4.3 Turning the Case Study into a Portfolio Piece3 items
- 4.3.1 Structuring the write-up: problem, approach, result, metrics🔒
- 4.3.2 Recording a short demo video: structure and pacing🔒
- 4.3.3 What to leave out for confidentiality, even in a fictional case study🔒
4.4 The FDE Interview: What They're Actually Testing For3 items
- 4.4.1 The real skills behind typical interview questions🔒
- 4.4.2 Common formats: live problem-solving, take-home, behavioral🔒
- 4.4.3 How to present the Meridian case study in an interview🔒
4.5 Practice Round: Live Problem-Solving Interview Simulation3 items
- 4.5.1 A brand-new, unseen domain presented cold🔒
- 4.5.2 Applying the Week 1 framework live, under time pressure🔒
- 4.5.3 Debrief: what transferred well, what needs more practice🔒
4.6 Your 30-Day Post-Course Plan: Landing the First Role3 items
- 4.6.1 Where to find FDE and FDE-adjacent roles🔒
- 4.6.2 Tailoring your portfolio and resume around the 4 artifacts🔒
- 4.6.3 A week-by-week outreach and application plan🔒
Hiring managers for forward deployed roles want someone who can read a client's real problem, build working software against it, and keep the relationship steady while they do. You already have the domain knowledge. This course adds the build skills and the client methods, and gives you a finished engagement to talk through in the interview.
From subscribing to running your first artifact
Subscribe and get immediate access
You get the full course, a free preview lesson to try before you commit, and access to daily live sessions and mentor review from day one. No software to install before you start, no prior technical setup required.
Open the course and join your simulated engagement
From the very first lesson you are working on one continuous client engagement that runs across all four weeks. You read real job postings, map the FDE role against its neighbours, and begin building your workbook log, all in plain steps that assume no coding background.
Build your first client artifact and add it to your portfolio
Within the first week you use Claude Code to produce a working tool aimed at a real client workflow, not a toy exercise. That artifact goes straight into your portfolio, and by week four you have a complete engagement case study a hiring manager can see running.
One FDE day on a client engagement: from observation to shipped tool
Morning: watch before you build
You spend the first ninety minutes sitting with the client analyst, watching how she actually works, not how the process map says she should. You notice she spends forty minutes every morning manually cross-referencing two data exports that nobody has ever connected. You log that observation in your workbook. You do not open a code editor yet.
Mid-morning: confirm the real blocker
You ask one clarifying question: is the cross-referencing the slow part, or is it knowing what to do with the result? She tells you the cross-referencing is manual and error-prone, and the result drives a decision her manager makes by noon. You now know exactly what to build and why the timing matters. You chase access to both data sources before you start.
Afternoon: build the targeted artifact
You use Claude Code to build a working agent that pulls both data sources, runs the cross-reference automatically, and surfaces the result in a format the analyst already trusts. Total build time: under two hours, because the morning's observation aimed every line. You do not add features she did not ask for.
Late afternoon: demo and correct
You show the tool to the analyst before her manager's noon decision the next day. She points to one field the tool sorts on that her team never trusts. You fix it in fifteen minutes. Without the demo you would have shipped the wrong sort order. The correction takes minutes because you are still embedded and the feedback is immediate.
End of day: update the sponsor in one sentence
You send the non-technical project sponsor a single plain-English line: the morning cross-reference the analyst was doing by hand now runs automatically and feeds into tomorrow's decision on time. No jargon, no status deck. The sponsor replies in two minutes. The artifact goes into your portfolio log the same evening.
The toolkit you'll build and run in this program
You leave with working assets, not notes.
Real things you can demo in an interview.
A complete portfolio case study covering one continuous simulated client engagement, with artifacts a hiring manager can see running
A role-boundary map (FDE vs ML Engineer vs Solutions Engineer vs Consultant) built from real job postings, ready to use in interviews
Working AI agents and tools built with Claude Code during the course, demonstrating technical capability without a prior coding background
A personal workbook insight log recording your discoveries, frameworks, and build decisions across all four weeks
A repeatable industry-reading method you can apply to any new client engagement from day one
Interview answer frameworks grounded in the course, including the FDE origin story and the role-distinction answer that interviewers specifically test
You'll get the most from this if
Procurement, supply-chain and operations people who understand client workflows and want the technical skills to move into AI deployment roles
Early-career analysts and recent graduates who want to combine domain knowledge with hands-on AI building
Solutions engineers and technical consultants who want a structured method and a portfolio for forward deployed work
People told they need years of coding before applying for AI roles who want a path that starts from zero
Yearly plan: 14-day keep-the-system guarantee
14-day refund on the yearly plan. Finish the first two modules, and if you haven't run your first live pipeline, take a full refund.
Straight answers before you subscribe
What is a forward deployed engineer?
A forward deployed engineer works inside a customer's organisation to get an AI product running on their real systems and data. It's part builder and part consultant: you watch how the client works, build what fixes the real blocker, demo it and keep the sponsor informed.
Is this course for experienced software engineers?
It's built for people coming from procurement, supply chain and operations, so it starts from zero on the technical side. Experienced engineers will find the client and engagement methods useful but will move through the build lessons quickly.
Do I need a coding or technical background to take this course?
No. The course is designed from the ground up for people who understand business and client problems but have never written production code. Every technical step is broken into plain instructions, you work with Claude Code in guided hands-on lessons from day one, and mentor review is available whenever you get stuck. If you can use a spreadsheet and follow a process, you have everything you need to start.
Will this work with the tools and setup I already have?
The course is built around Claude Code and MCP, which run in a browser-accessible environment with no specialist hardware required. You do not need a pre-existing enterprise system or a company data warehouse. The simulated client engagement gives you everything you need to practise against real-feeling scenarios from your own laptop.
What if I try it and it is not right for me?
Refunds depend on the plan. The yearly plan has a 14-day full refund. In India every plan is a one-time pass that isn't refunded, but it never renews without you, and you can try a free lesson before you buy. The refunds page has the full policy.
Can I use what I build in this course in my own work or show it to employers?
Yes. The artifacts, workbook, and portfolio case study you build are yours. You can show them to potential employers, use the methods in your current role, and demonstrate the tools in interviews. The course licence covers personal professional use, so everything you produce during the engagement is yours to keep and deploy.