Go from junior developer to AI Engineer: build reliable systems on foundation models and get hired for the role
A four-week, build-everything course for developers who want to stop wondering how production AI systems actually work and start shipping them, with four deployed portfolio artifacts and real numbers to show in an interview.
30 chapters, 98 lessons
14-day refund on the yearly plan. Real pricing on the plans page, no surprises.
98
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.
Think and speak like an AI Engineer from day one
- You'll analyse real job postings and articulate exactly what separates an AI Engineer from an ML Engineer or Data Scientist, in the words a hiring manager uses.
- You'll decompose any vague product ask into the four concrete sub-problems every AI Engineer is accountable for: load, retrieve, ground, and refuse.
- No prior AI or ML background needed: the course opens with the orientation every self-taught developer wishes they'd had before their first interview.
Build a hardened model client that belongs in production
- You'll wire up a model client that handles retries, timeouts, streaming, and token budgets correctly, the way a senior engineer would review it.
- You'll write and version system prompts with grounding and refusal instructions baked in from the start, not bolted on after a hallucination incident.
- These are skills a hiring manager sees on a CV and immediately recognises as production-ready, not academic.
Build a grounded RAG pipeline with hybrid search and citations
- You'll ingest, chunk, and index 40,000 messy contracts and build the retrieval layer that fetches the right passage before the model ever sees a question.
- You'll add hybrid search, re-ranking, and source citations so answers are verifiable, which is the difference between a demo and a system a legal team will trust.
- You'll handle this hands-on from lesson one: every step is plain code with a clear explanation, no machine-learning theory required.
Connect models to real systems and ship with confidence
- You'll implement function calling and MCP so the model can query a live database, trigger a workflow, and return a grounded answer rather than a guess.
- You'll add prompt-injection defences, cost controls, latency engineering, distributed tracing, and a regression eval suite gated in CI, the full production checklist.
- Every one of those capabilities appears verbatim in AI Engineer job descriptions, and you'll have a deployed artifact to prove you've actually built it.
Finish this course and you can do all of this, no prior background required:
- You'll be able to design and build a production RAG pipeline with hybrid search and citations on a real document corpus, the capability listed in nearly every senior AI Engineer posting.
- You'll be able to implement function calling and MCP to connect a language model to live databases and external workflows, giving you the integration depth teams need when they move past demos.
- You'll be able to instrument a live AI system with distributed tracing, interpret the traces to diagnose retrieval and grounding bugs, and fix them without guessing at the model.
- You'll be able to build a regression eval suite gated in CI so every code change is automatically tested against a benchmark, the reliability practice that separates production engineers from prompt t
- You'll be able to apply prompt-injection defences, cost controls, and latency engineering to a shipped system, the hardening checklist that makes a hiring manager confident you've seen a production in
- You'll be able to walk into any AI Engineer interview with four deployed artifacts and real latency and cost numbers, giving you concrete answers to every how would you actually build it question.
98 lessons, 30 chapters
Built by practitioners, not influencers
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. Your system gets better, not obsolete.
You've been watching AI Engineering roles appear on every job board and you know your backend skills are close enough, but every posting asks for RAG pipelines, eval suites, and production tracing, and you've never built any of them. You apply anyway and hear nothing back, because without a deployed artifact showing real numbers there's no way to distinguish you from the hundreds of candidates who've only read the blog posts. Meanwhile the gap between what the role demands and what a bootcamp or YouTube tutorial teaches keeps widening. You need to build the actual thing, on a realistic dataset, with the reliability engineering that makes it worth hiring for, not another chatbot demo that breaks the moment a real user touches it.
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 · 98 lessons
Orientation (Pre-Week 1)
You can articulate the AI Engineer role in the exact language hiring managers use, distinguish it from ML Engineer, Data Scientist, and Backend Engineer from a single product ask, and decompose any vague brief into the four sub-problems you are accountable for: load, retrieve, ground, and refuse.
0.1 What Is an AI Engineer, Really?4 items
- 0.1.1: How the role emerged: from ML teams that trained models to product teams that assemble them🔒
- 0.1.2: AI Engineer vs. ML Engineer vs. Data Scientist vs. Backend Engineer, where the lines actually are🔒
- 0.1.3: A real day-in-the-life walkthrough, hour by hour🔒
- 0.1.4: Why 'I've used ChatGPT and know Python' is not the same skill, and what the real gap is🔒
0.2 Meet the Case Study: Juniper Legal Wants a Contract Chatbot4 items
- 0.2.1: The managing partner's stated ask (what the client thinks they need)🔒
- 0.2.2: Firm background, the 40,000-document archive, and why 'just a chatbot' hides the real problem🔒
- 0.2.3: The stakeholders you'll deal with (partner, IT lead, a skeptical associate, the paralegal who knows the archive)🔒
- 0.2.4: What 'success' looks like to a law firm, and why a confident wrong answer is worse than no answer🔒
0.3 The 4-Week Roadmap3 items
- 0.3.1: Skill map: what is added each week and why in that order (call, ground, connect, harden the model)🔒
- 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 (a frontier model API key, git plus GitHub, Python 3.11+, a vector store, a deploy target)🔒
- 0.4.2: Creating THE repository: the single repo the whole course builds inside🔒
- 0.4.3: A first 'hello model' test: one API call from code with proper key handling🔒
- 0.4.4: Troubleshooting common setup failures (auth errors, version conflicts, rate limits on free tiers)🔒
Week 1: Talking to Models From Code, Properly
You can build and ship a hardened model client with correct retry logic, streaming, token budgeting, and a versioned system prompt that grounds the model's answers and enforces refusal, ready for a senior engineer's code review.
1.1 The API Layer: What You Actually Control3 items
- 1.1.1: Anatomy of a model call: system prompt, messages, temperature, max tokens, and what each one changes🔒
- 1.1.2: The request that costs $0.002 and the request that costs $2: tokens as the unit of everything🔒
- 1.1.3: A real dissected example: one production API call from a shipped product, parameter by parameter🔒
1.2 System Prompts Are Software Contracts4 items
- 1.2.1: Writing a system prompt like an interface spec: role, constraints, refusal rules, output shape🔒
- 1.2.2: The instruction-following stress test: 10 adversarial user messages against your contract🔒
- 1.2.3: Practice pass: writing the v1 Juniper system prompt that refuses to answer without a source🔒
- 1.2.4: Common trap: stuffing the system prompt with vibes instead of testable rules🔒
1.3 Structured Outputs: Making Models Return Data, Not Prose3 items
- 1.3.1: Why 'please respond in JSON' fails 4% of the time and why 4% is catastrophic🔒
- 1.3.2: Schema-enforced outputs: defining the shape and letting the API guarantee it🔒
- 1.3.3: Parsing, validating, and handling the cases where structure still surprises you🔒
1.4 Error Handling, Streaming, and the Unhappy Paths3 items
- 1.4.1: The four failures every LLM app must survive: rate limits, timeouts, overloaded errors, refusals🔒
- 1.4.2: Retries with backoff, fallback models, and graceful degradation, implemented🔒
- 1.4.3: Streaming responses: when users need it and how to wire it🔒
1.5 Case Study Checkpoint: Juniper's Naive Prototype, and Its First Lie3 items
- 1.5.1: Building the no-retrieval v0: system prompt plus user question, straight to the model🔒
- 1.5.2: The hallucinated clause: watching v0 invent 'Section 14.2' with total confidence🔒
- 1.5.3: Writing the engineering problem statement: why this is a grounding problem, not a prompting problem🔒
1.6 Week 1 Portfolio Artifact: A Hardened Model Client3 items
- 1.6.1: Packaging Weeks 1.1 to 1.4 into a reusable client module: structured outputs, retries, cost logging🔒
- 1.6.2: The README that proves you understand it: documenting each design decision🔒
- 1.6.3: Checklist: what makes this module interview-ready🔒
Week 2: Grounding, RAG, the Pattern That Runs the Industry
You can ingest and chunk a corpus of messy documents, build a hybrid search index, retrieve the right passage for any question, attach source citations to every answer, and tune the pipeline so a legal team can verify what the model says.
2.1 Ingestion: Where RAG Quality Is Actually Decided4 items
- 2.1.1: The messy-document reality: scans, duplicates, headers mid-sentence, three date formats🔒
- 2.1.2: Chunking strategies and what each one breaks (fixed-size, semantic, structure-aware)🔒
- 2.1.3: Hands-on: ingesting the Juniper contract pack and inspecting what your chunker produced🔒
- 2.1.4: Metadata is not optional: dates, parties, document types as first-class fields🔒
2.2 Embeddings and Vector Search, Without the Math Lecture3 items
- 2.2.1: What an embedding buys you, shown through queries that keyword search cannot answer🔒
- 2.2.2: Standing up the vector store and indexing the Juniper chunks🔒
- 2.2.3: Similarity search hands-on: the queries that work brilliantly and the ones that fail embarrassingly🔒
2.3 Hybrid Search and Reranking: What Production Actually Uses3 items
- 2.3.1: The exact-term failure: why pure vector search whiffs on clause numbers, party names, defined terms🔒
- 2.3.2: Combining keyword (BM25) and vector results, and reranking the merged list🔒
- 2.3.3: Measuring the difference: a 20-query retrieval eval, before and after🔒
2.4 Grounded Generation: Citations, Refusals, and 'I Don't Know'3 items
- 2.4.1: Wiring retrieved chunks into the prompt with source IDs the model must cite🔒
- 2.4.2: The grounding check: verifying every claim maps to a retrieved chunk🔒
- 2.4.3: Making 'I couldn't find that in the archive' a feature, not a failure🔒
2.5 Case Study Checkpoint: Juniper's Pipeline Meets the Real Archive3 items
- 2.5.1: Running the full pipeline on the ugliest documents in the pack🔒
- 2.5.2: The failure cluster review: where retrieval broke, where grounding broke, and telling them apart🔒
- 2.5.3: The iteration pass: fixing the top failure and re-running the 20-query eval as proof🔒
2.6 Side Quest: Same Pipeline, Different Universe3 items
- 2.6.1: Re-pointing the pipeline at a second corpus from an unrelated domain (a support-article pack)🔒
- 2.6.2: What transfers untouched, what needs re-chunking, what needs new metadata🔒
- 2.6.3: Reflection: writing the one-page 'RAG pattern vs. RAG instance' note🔒
2.7 Week 2 Portfolio Artifact: A Deployed RAG Service With Receipts3 items
- 2.7.1: Wrapping the pipeline in a minimal API endpoint and deploying it🔒
- 2.7.2: The eval report: 20 queries, retrieval scores, and three documented failure cases with fixes🔒
- 2.7.3: Checklist: what makes this artifact credible to a technical reviewer🔒
Week 3: Connecting Models to Real Systems
You can implement function calling and MCP to let a model query a live database or trigger a workflow, add cost controls and latency engineering so the system stays within budget under load, and instrument it with distributed tracing so you can diagnose a retrieval bug in minutes.
3.1 Function Calling: The Model Decides, Your Code Executes4 items
- 3.1.1: The pattern: tool schemas, model tool-choice, execution, and result return🔒
- 3.1.2: Building Juniper's first tool: a structured contract-metadata lookup the model can call🔒
- 3.1.3: Multi-tool calls and the loop: letting the model chain lookups to answer compound questions🔒
- 3.1.4: Common trap: tools that mirror your database instead of the user's intent🔒
3.2 MCP: The Standard for Giving Models Access4 items
- 3.2.1: What MCP is in plain terms, and why 'write a custom integration per app' died🔒
- 3.2.2: Consuming an existing MCP server from your application🔒
- 3.2.3: Authoring a minimal MCP server that exposes the Juniper archive as tools🔒
- 3.2.4: Troubleshooting connection, schema, and permission failures🔒
3.3 Security: You Gave a Language Model Database Access3 items
- 3.3.1: Prompt injection through documents: the poisoned contract that rewrites your model's instructions🔒
- 3.3.2: Least-privilege tool design: read-only by default, allowlists, and human confirmation for writes🔒
- 3.3.3: Input and output guardrails: what to filter, what to log, what to block🔒
3.4 Cost and Latency Engineering3 items
- 3.4.1: The cost model of the full pipeline: where the money actually goes (not where beginners think)🔒
- 3.4.2: Prompt caching, model-tier routing, and context trimming, implemented and measured🔒
- 3.4.3: Latency budgets: what to parallelize, what to cache, what to precompute🔒
3.5 Case Study Checkpoint: Juniper Goes Live-ish, and the Bills and Threats Arrive3 items
- 3.5.1: The twist: IT flags a prompt-injection finding, and the projected monthly cost lands at 6x budget🔒
- 3.5.2: Hardening pass: injection defenses and least-privilege tools, applied🔒
- 3.5.3: Cost pass: caching and routing cut the projection, with before/after numbers🔒
3.6 Observability: Tracing What the Model Actually Did3 items
- 3.6.1: Instrumenting the pipeline: traces for every retrieval, tool call, and generation🔒
- 3.6.2: Reading a trace to debug a wrong answer in minutes instead of hours🔒
- 3.6.3: The metrics that matter in production: cost per query, grounding rate, refusal rate, p95 latency🔒
3.7 Week 3 Portfolio Artifact: A Connected, Guarded, Traced System3 items
- 3.7.1: Packaging the tool layer, MCP server, guardrails, and tracing into the repo🔒
- 3.7.2: The security note: documenting your injection defenses and privilege model🔒
- 3.7.3: Checklist: what makes this artifact credible to a security-conscious reviewer🔒
Week 4: Hardening, Shipping, and Getting Hired
You can defend a production system against prompt-injection attacks, build a regression eval suite gated in CI so regressions never reach users, and present four deployed portfolio artifacts with real performance numbers that answer every technical question an interviewer can raise.
4.1 The Regression Eval Suite: Your Ship/No-Ship Machine3 items
- 4.1.1: Turning Week 2's 20 queries and every bug since into a permanent golden set🔒
- 4.1.2: Automated eval runs on every change: wiring the suite into CI🔒
- 4.1.3: Kill criteria: the score below which a change does not merge, decided in advance🔒
4.2 Deployment: From Repo to Running Service3 items
- 4.2.1: Containerizing the service and deploying it properly (env, secrets, health checks)🔒
- 4.2.2: Rollout discipline: staging, smoke tests, and the rollback you rehearse before you need it🔒
- 4.2.3: The incident drill: an injected production failure, diagnosed from traces alone🔒
4.3 Case Study Checkpoint: Juniper Legal, Final Delivery3 items
- 4.3.1: The final twist: a partner's demo question triggers a subtle grounding failure, caught by the eval suite🔒
- 4.3.2: Assembling everything from Weeks 1 to 3 into the final handoff build🔒
- 4.3.3: Final validation against the original AND revised problem statements, and the handoff summary🔒
4.4 Turning the Case Study into a Portfolio Piece3 items
- 4.4.1: Structuring the write-up: problem, architecture, decisions, metrics, failures survived🔒
- 4.4.2: Recording a short demo video: structure and pacing, including showing one failure on purpose🔒
- 4.4.3: What to leave out for confidentiality, even in a fictional case study🔒
4.5 The AI Engineer Interview: What They're Actually Testing For4 items
- 4.5.1: The real skills behind typical questions (system design, 'walk me through your eval', live debugging)🔒
- 4.5.2: Common formats: take-home build, live pairing, architecture whiteboard, behavioral🔒
- 4.5.3: How to present the Juniper build in an interview🔒
- 4.5.4: Practice round: a brand-new, unseen product brief, architecture sketched cold under time pressure🔒
4.6 Your 30-Day Post-Course Plan: Landing the First Role3 items
- 4.6.1: Where to find AI Engineer roles, and how to spot real ones vs. rebadged data-entry listings🔒
- 4.6.2: Tailoring your portfolio and resume around the 4 artifacts (deployed links first, always)🔒
- 4.6.3: A week-by-week outreach and application plan🔒
Recruiters screening AI Engineer candidates in 2026 are flooded with people who have prompted ChatGPT and called it experience: the candidate who shows up with a deployed RAG pipeline, a CI-gated eval suite, and real latency numbers from a 40,000-document corpus is the one who ends the search. Stacking the full production stack from this course, grounding, retrieval, function calling, tracing, and
From subscribing to running your first artifact
Subscribe and unlock the full course immediately
The moment you subscribe you get access to every module, the Juniper Legal case-study dataset, the starter code repository, and the daily live sessions with mentor review. No waiting for weekly drip, no prerequisites beyond basic coding familiarity.
Open Orientation and build your interview bank from lesson one
You start by pulling real AI Engineer job postings and writing the one sentence that distinguishes this role from every other engineering seat. By the end of Orientation you have a working framework, load, retrieve, ground, refuse, that structures every design decision you'll make across the next four weeks.
Build and deploy your first artifact against 40,000 real contracts
Week 1 starts with a hardened model client talking to a live API: retries, streaming, token budgets, a grounded system prompt. You run it against the Juniper Legal dataset, see real latency and cost numbers, and commit your first portfolio artifact before the week is out.
Juniper Legal asks: which of our contracts auto-renew, and when?
Load and chunk 40,000 messy contracts
You ingest the Juniper archive, handle scanned PDFs with inconsistent formatting, and chunk each document so retrieval works at clause level rather than whole-contract level. This is the load sub-problem: the model cannot see documents it was never given.
Build a hybrid search index and retrieve the right clauses
You build a hybrid index combining dense vector search with keyword search, then query it for auto-renewal language across all 40,000 files. The retriever returns the ten most relevant clause passages ranked by relevance, not the ten most recent contracts.
Ground the model's answer in retrieved passages and attach citations
You pass the ranked passages to the model with a system prompt that instructs it to answer only from the provided text and to cite the contract name and clause number for every claim. The model cannot drift into general contract knowledge it picked up during training.
Enforce refusal when the archive does not contain the answer
You test a question about a contract type Juniper does not hold. The grounded prompt and refusal instruction cause the model to say the archive does not contain a matching clause rather than fabricate a plausible-sounding date, which is what the unguarded model would do.
Instrument with tracing and run the eval suite in CI
You trace the full request, retrieve, prompt assembly, model call, and response parsing, so when a clause is retrieved incorrectly you can see exactly which step failed. The regression eval suite runs on every commit so a chunking change that degrades retrieval precision is caught before it reaches the Juniper team.
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 hardened model client with retry logic, streaming, and token budgeting, deployable and reviewable as a standalone portfolio artifact.
A full RAG pipeline built on 40,000 Juniper Legal contracts, with hybrid search, re-ranking, and source citations, demonstrating grounding at realistic scale.
A function-calling and MCP integration connecting a language model to a live data source, with cost and latency instrumentation included.
A regression eval suite wired into CI, showing a hiring manager that your system catches regressions automatically before they reach users.
A personal interview bank built across every module, mapping each technical capability to the job-posting language that will get you past a screening call.
You'll get the most from this if
Junior or mid-level developers who can write basic code and want to move into AI Engineering with a portfolio that proves it.
Backend or full-stack engineers who've been asked to add AI features to a product and need to build them properly, not just wrap a chat API.
Self-taught developers who've followed the blog posts and tutorials but have never built a production-shaped system they could show in an interview.
Recent graduates with a computer science or software background who want their first role to be in AI Engineering, not a generic dev position.
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
Do I need a coding background or any prior AI experience?
You need to be comfortable writing basic code, reading a function, and running a script, but no AI, ML, or data-science background is assumed. The course is designed for developers making the move into AI Engineering: every concept is introduced with a plain explanation before you write a line, and the Orientation module specifically covers the role from first principles. No GPUs, no PyTorch, no gradient maths.
Will this work with my current setup, laptop, operating system, editor?
The course is built around API calls and standard Python tooling that runs on any reasonably modern laptop on macOS, Windows, or Linux. There is no local GPU requirement and no proprietary infrastructure to stand up before you can start. The starter repository is set up so your first artifact runs in the same session as your first lesson.
What if I get stuck or fall behind during the four weeks?
The course includes daily live sessions and mentor review of your work, so you have a named human to ask when something is not clicking. Because you get access to all modules immediately on subscribing, you can move at your own pace and use the live sessions to unblock yourself wherever you are in the material, not just at the week boundary.
How does the course stay current as the AI Engineering landscape moves fast?
Updates to the course content are included in your subscription. When a lesson references a tool or API pattern that has changed materially, the lesson is revised and you receive the updated version. The curriculum is grounded in the durable patterns, retrieval, grounding, evals, tracing, that have been stable across multiple waves of model releases, so the core skills age well even as specific APIs evolve.
What is the refund policy, and can I use what I build commercially?
There is a 14-day refund window from the date of purchase: if you go through the material and it is not right for you, you can request a full refund within that period. Everything you build during the course, the model client, the RAG pipeline, the eval suite, the deployed artifacts, is yours to use, including in commercial projects and as portfolio work in job applications.