production-ai-engineer · Cohort opens 3 Jun 2026
Production AI Engineer
A 14-week program that turns a working software engineer into a Production AI Engineer.
- Duration
- 14 weeks
- Cohort seats
- 0
- Next start
- 3 Jun 2026
- Status
- published
Overview
A 14-week program that turns a working software engineer into a Production AI Engineer.
The cohort runs for 14 weeks. Two live sessions per week, asynchronous mentor review on every commit, a capstone artifact that the credential links to. Pre-cohort prework is gated on the entrance assessment.
Who this is for
- Engineers with 2+ years of production code who want to ship LLM-backed features that survive on-call rotation.
- Backend or platform engineers moving into an AI-adjacent role and tired of demos that do not generalise.
- Tech leads who need to evaluate, deploy, and govern a model-serving stack at their employer.
Who this is not for
- Total beginners — the entrance assessment expects working Python, HTTP, and version control.
- Pure researchers — the capstone is a deployed system, not a paper.
- Anyone hoping to skip the prework. Pre-cohort modules are gated; cohort week one assumes them.
What you'll build
- 01A retrieval system with measurable groundedness and a CI eval harness that fails the build on regression.
- 02A tool-using agent loop with budget limits, structured logging, and a runbook a teammate can follow at 2am.
- 03A model-serving deployment behind a real load balancer, with rollout, rollback, and cost telemetry.
- 04A capstone repository — code, evals, runbook, postmortem — that the public ledger links to from your credential.
Cohort schedule
- Cohort opens
- Wednesday, 3 June 2026
- Enrolment closes
- Wednesday, 20 May 2026
- Capstone due
- 2026-09-09 (week 14)
- Cadence
- Two 90-min live sessions per week + one office hour
- Timezones
- Cohort runs in two timezone bands: Americas/EU and EU/APAC
Week by week
14 weeks
Week 01
Eval-first development
Artifact Golden-set v1 + a failing CI test that passes locally
Week 02
Retrieval that survives review
Artifact Indexed corpus + groundedness scorer wired to CI
Week 03
Tool-use loops without runaway cost
Artifact Agent loop with budget limit, audit log, replay harness
Week 04
Structured logging and audit trails
Artifact OpenTelemetry trace export from a live request path
Week 05
Mid-cohort calibration review
Artifact 1:1 mentor review of evals + decision log signed off
Week 06
Model serving and rollout strategy
Artifact Canary rollout plan + automated rollback runbook
Week 07
Cost, latency, and capacity planning
Artifact Per-request cost dashboard + SLO doc
Week 08
Safety, refusal policy, abuse handling
Artifact Refusal taxonomy + red-team report
Week 09
Postmortem culture and on-call
Artifact Written postmortem on a synthetic incident
Week 10
Capstone scoping and acceptance criteria
Artifact Capstone spec accepted by mentor and a peer reviewer
Week 11
Capstone build · part 1
Artifact Working v1 with eval harness on main branch
Week 12
Capstone build · part 2
Artifact Deployed v1 with monitoring + a published runbook
Week 13
External review
Artifact Two external engineers review the repo against rubric
Week 14
Capstone defence and credential issuance
Artifact Signed W3C VC + indexed entry in the public ledger
Mentors on this cohort
4 attached
Anika Rao
Staff AI Engineer · ex-Stripe Risk
Built and ran the eval pipeline for a payments-fraud LLM feature serving 40M requests/day. Mentors on retrieval and eval calibration.
Dr. Marcus Hoel
Principal ML Engineer · ex-Anthropic Applied
Shipped tool-use agents into a production support workflow at a F500 insurer. Mentors on agent loops and budget governance.
Priya Nair
Platform Lead · ex-Datadog
Owned the model-serving control plane for an observability product. Mentors on deployment, rollback, and cost accounting.
Joaquín Mendes
Founding Engineer · independent
Author of the open-source eval harness used in the cohort capstone. Mentors on CI integration and regression detection.
Each mentor commits to a maximum of six learners per cohort. Mentor calibration data is shared with mentors directly; the aggregate distribution is on the transparency report.
Tuition
Regional pricing — tuition is set by the region of residence, not by employer. The outcome credit column is the percentage of tuition refunded if you do not place against the stated outcome inside the published 90-day window.
| Tier | Region | Tuition | Outcome credit |
|---|---|---|---|
| standard | us eu apac | $799.00 | 60% |
| standard | india | $299.00 | 60% |
| standard | latam sea africa | $399.00 | 60% |
Outcome credential
On capstone defence we issue a W3C Verifiable Credential with an Open Badges 3.0 assertion, signed by the issuer key listed in the public JWKS. Employers verify it from a public URL — no portal account required.
The credential carries the capstone repository URL, the mentor sign-off, and the published rubric. Anything that is not on the credential is not part of the claim.
Questions engineers usually ask
- What does the entrance assessment actually test?
- Three things: a 60-minute Python + HTTP coding exercise, a short written reasoning task on a system trade-off, and a 25-minute conversation with a mentor. The rubric is published; we do not test trivia.
- Can I take this part-time while employed?
- Yes. The cohort is built for working engineers — 12–15 hrs/wk, two live sessions per week, asynchronous reviews. Fewer than 8% of past learners reduced employer hours during the cohort.
- What happens if I do not finish the capstone in 14 weeks?
- You may defer once to the next cohort at no additional tuition. A second deferral converts the seat to audit access. The full policy is in the enrolment agreement.
- Is the credential recognised by employers?
- The credential is a W3C Verifiable Credential with an Open Badges 3.0 assertion and is signed by the issuer key listed in our JWKS. Employers verify it from a public URL — no portal account required.
- What is the outcome credit clause?
- If you do not place against the stated outcome band within the published 90-day window, the percentage of tuition listed under the outcome credit column on the tuition table is refunded automatically.
Next step
Take the entrance assessment for Production AI Engineer.
90 minutes. Coding, written reasoning, a short conversation with a mentor. The rubric is published. Pass and you receive an enrolment offer for the cohort opening 3 Jun 2026.
Production AI Engineer · Cohort opens 3 Jun 2026
Apply to the cohort opening 3 Jun 2026