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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

  1. Week 01

    Eval-first development

    Artifact Golden-set v1 + a failing CI test that passes locally

  2. Week 02

    Retrieval that survives review

    Artifact Indexed corpus + groundedness scorer wired to CI

  3. Week 03

    Tool-use loops without runaway cost

    Artifact Agent loop with budget limit, audit log, replay harness

  4. Week 04

    Structured logging and audit trails

    Artifact OpenTelemetry trace export from a live request path

  5. Week 05

    Mid-cohort calibration review

    Artifact 1:1 mentor review of evals + decision log signed off

  6. Week 06

    Model serving and rollout strategy

    Artifact Canary rollout plan + automated rollback runbook

  7. Week 07

    Cost, latency, and capacity planning

    Artifact Per-request cost dashboard + SLO doc

  8. Week 08

    Safety, refusal policy, abuse handling

    Artifact Refusal taxonomy + red-team report

  9. Week 09

    Postmortem culture and on-call

    Artifact Written postmortem on a synthetic incident

  10. Week 10

    Capstone scoping and acceptance criteria

    Artifact Capstone spec accepted by mentor and a peer reviewer

  11. Week 11

    Capstone build · part 1

    Artifact Working v1 with eval harness on main branch

  12. Week 12

    Capstone build · part 2

    Artifact Deployed v1 with monitoring + a published runbook

  13. Week 13

    External review

    Artifact Two external engineers review the repo against rubric

  14. 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.

TierRegionTuitionOutcome credit
standardus eu apac$799.0060%
standardindia$299.0060%
standardlatam sea africa$399.0060%

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.

See verified credentials in the public ledger

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