Forward Deployed Engineer Course: From Discovery to AI Implementation

Who this is for

• Engineers who have shipped with LLM APIs and retrieval and now need to deliver inside a customer's constraints
• Solutions and platform engineers moving into forward deployed, customer-facing delivery
• Founders and consultants who build AI systems for clients and want them to survive the client's security review
• Graduates of our Vibe Code courses ready for an advanced, Python-first engagement

1. 01

   Saturday, November 14

   Discovery and Scoping

   Interview three conflicting stakeholders live, then write the one-page engagement spec: first slice, a success metric that is a number, real non-goals, cost and latency ceilings, and kill criteria.

2. 02

   Saturday, November 21

   Customer Data and Access

   Build adapters over three messy sources, decide what to do with PII, carry document permissions into every chunk, and write the data findings memo the customer will actually read.

3. 03

   Saturday, November 28

   Prototype and Evaluation

   Build a grounded prototype on a service layer that survives real providers, with permission-aware retrieval, citations enforced in code, and a refusal path. Then prove it works: a golden set built with the customer, a judge calibrated against human labels, automated tests that catch regressions, and a red-team report against the OWASP LLM Top 10.

4. 04

   Saturday, December 5

   Agents in Customer Systems

   An agent loop built by hand, tools wrapping real customer actions, approval gates and idempotency on irreversible operations, step and spend caps, full tracing, and trajectory-level evaluation.

5. 05

   Saturday, December 12

   Deploy, Operate and Hand Over

   Deploy inside private networking and least-privilege identity, define four alarms, survive a simulated outage drill, price the system per unit of work, and hand over a pack an internal team can run without you.

6. 06

   Two to three weeks after Session 5

   Capstone

   Pick one of four industry briefs (healthcare, pharma and life sciences, retail and CPG, financial services), run the full engagement solo or in a pair, and present a twenty-minute readout to a mock steering committee.

SH

Shadab Hussain

Lead Engineer, Data & GenAI, MathCo

Shadab is a technology executive and strategic advisor specializing in AI and quantum computing. As Lead Engineer at MathCo he architects enterprise AI solutions for Fortune 500 clients. He was previously a Developer Advocate at the London Stock Exchange Group. He is a Google Developer Expert in AI/ML and a TEDx speaker, holds the AWS Certified Generative AI Developer Professional certification, and co-founded Quantum Computing India, the nation's largest quantum community.

Grant Kurz

Founder & CEO, DeepStation AI

Software engineer and entrepreneur who builds AI systems for enterprise and universities, and founded DeepStation, a global AI community of 6,000+ industry professionals with 100+ workshops and speakers including Sergey Brin, Co-Founder of Google. His technical work spans RAG systems at Coca-Cola and Stanley Black & Decker, a crypto analytics platform (400,000+ users, acquired by CoinLedger.io), and an AI Call Center solution for a major U.S. political campaign. Featured in Forbes, Coindesk, and OpenAI's blog.

Meet the whole team

• Six engagement artifacts written against a live simulated customer: engagement spec, data findings memo, grounded prototype and demo script, evaluation harness and red-team report, tool-using agent with an adversarial suite, runbook and handover pack
• A working system deployed on AWS or Google Cloud inside private networking and least-privilege identity
• A capstone engagement on a real industry brief, presented to a mock steering committee
• A coding-agent skill library covering discovery, ingestion and access, evaluation, review discipline, and handover
• The judgment to scope a first slice, say no to the rest, and defend the work to a security reviewer