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Career route 1

Customer AI Deployment

For roles like Forward-Deployed AI Engineer, Field AI Engineer, AI Solutions Engineer

Discover a customer workflow, reduce its riskiest assumptions, and carry a useful AI system through measurement and rollout.

12 lessons about 14 h of lessons Ends in a certificate Paid plans
Lesson checks passed0 of 12
Start: Define the Outcome Before You Choose the Output

Is this route for you?

Do you want to discover a customer workflow, prove a narrow solution, and carry it into a measured rollout?

  • You want to work close to users and ambiguous operating environments.
  • You enjoy moving between discovery, implementation, evaluation, and rollout.
  • You can explain tradeoffs to both technical and operational stakeholders.

What you'll be able to do: Build on shared foundations to turn an observed customer workflow into a scoped, evaluated, and operable AI deployment.

Start with

  • Complete the shared software engineering and AI foundations, or demonstrate equivalent working knowledge.
  • Be able to build and evaluate an API-backed AI application.
  • Be comfortable gathering workflow evidence before proposing a solution.

The route

Common CoreFrame the customer outcome, workflow, assumptions, and smallest useful test.
  1. 1Define the Outcome Before You Choose the OutputPhase 14: Agent engineering · 1 h
  2. 2Discover the Workflow People Actually PerformPhase 14: Agent engineering · 1 h
  3. 3Map Assumptions and Resolve the Riskiest One FirstPhase 14: Agent engineering · 1 h
  4. 4Choose the Smallest Slice That Can Change the DecisionPhase 14: Agent engineering · 1 h
  5. 5Write Specifications That Preserve JudgmentPhase 14: Agent engineering · 1.5 h

You produce: A workflow brief with outcome, evidence, assumptions, risks, and an executable slice specification.

Role PracticeBuild and evaluate the narrow AI system against explicit success criteria.
  1. 6RAG (Retrieval-Augmented Generation)Phase 11: LLM engineering and RAG · 1.5 h
  2. 7Evaluation & Testing LLM ApplicationsPhase 11: LLM engineering and RAG · 45 min
  3. 8Building a Production LLM ApplicationPhase 11: LLM engineering and RAG · 2 h
  4. 9Design Success Metrics Before the Result ExistsPhase 14: Agent engineering · 1 h

You produce: A working vertical slice with an evaluation set, baseline, and success scorecard.

Proof ProjectChoose the right release stage and move the pilot through controlled exposure.
  1. 10Choose Prototype, Pilot, or Production DeliberatelyPhase 14: Agent engineering · 1 h
  2. 11Shadow Traffic, Canary Rollout, and Progressive Deployment for LLMsPhase 17: Infrastructure and production · 1 h

You produce: A measured pilot with release gates, rollback conditions, and recorded results.

Interview and Readiness EvidenceShow how evidence and user feedback determine the next iteration.
  1. 12Build a Feedback Ratchet with Ownership and RetirementPhase 14: Agent engineering · 1.5 h

You produce: A concise case study that connects user evidence, system results, tradeoffs, and the next decision.

Portfolio project: Measured Customer Pilot

Turn a documented workflow into a narrow pilot with explicit evidence, release controls, and a feedback plan.

  • Workflow evidence and an assumption map
  • An executable specification for the smallest testable slice
  • A working vertical slice with evaluation results
  • Success metrics and a staged rollout plan
  • A feedback record that drives the next decision

You're ready when you can

  • Separate the requested output from the underlying operational outcome.
  • Rank assumptions by uncertainty and consequence.
  • Build a vertical slice that tests one valuable workflow end to end.
  • Define evaluation, success, rollback, and feedback criteria before release.
  • Explain what evidence supports moving from prototype to pilot or production.

What this route covers, honestly

Covered in depth
  • Workflow discovery and scoping
  • Evidence-driven prototyping
  • Evaluation and success metrics
  • Staged rollout and feedback
Partly covered
  • Stakeholder facilitation
  • Enterprise integration and security
  • Production operations under live traffic
Outside the course
  • Commercial account ownership
  • Procurement and legal negotiation
  • Years of customer deployment experience

Customer AI Deployment career route certificate

Pass all 12 lesson checks on this route (0 done), then the 20-question route exam drawn from these lessons. The certificate lists every topic you covered and carries an ID anyone can verify.

Exam requirements

Completing this route shows that you can produce evidence for a scoped customer AI pilot. It does not guarantee a role or establish production experience. This is a specialist overlay after shared foundations, not customer support or generic consulting. It prepares you to demonstrate a deployment workflow, but it does not replace live customer or production experience. The estimate covers lesson time only. Portfolio work and practice take additional time.