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The complete learning route

From understanding the work
to owning the delivery.

A forward-deployed engineer turns an organization's difficult workflow into a useful, dependable system—and equips other people to own it. This route connects business understanding, software engineering, AI, human judgment and service operations around one project.

6 connected stages12 capability areasShared core + cloud and industry specializations
Find your starting point Developer, architect or new to software

New to software or AI

Start with the vocabulary and engineering workbench. Build and test a small program before moving into agents. Use the 101 explanations whenever a concept is unfamiliar.

Open the first principles library →

Practising developer

Use a reviewed change to establish your coding baseline. Give equal attention to discovery, evaluation, human decisions and receiving ownership—the parts a coding tool cannot decide for you.

Begin with the client workflow →

Architect or technical lead

Bring your systems experience, then demonstrate an actual implementation and recovery. Treat diagrams as claims to verify, and connect platform choices to workload and operating responsibility.

Inspect the integration proof →

Stage 5 of 6

Operate with people

Keep the service dependable and make the human workflow understandable.

09

Reliability, release & incident operations

Can the service survive a bad day without depending on its author?

Define reliability around completed work. Understand capacity and overload, identify exactly what was deployed, and rehearse recovery with compatible data and a receiving owner.

Builds onEvaluation science & economic reasoningSecurity, privacy & AI governanceCloud, compute & infrastructure delivery

Illustrative learning artifact

Reliability belongs to the user task

Defined good eventSuccess and within 2 seconds
1,000eligible requests
10bad-event allowance at 99%
12observed bad events

Two beyond the allowance. Investigate the failed work.

What you need to understand

  • SLIs, SLOs, error budgets and observability
  • Capacity, backpressure and dependency limits
  • Provenance, dependency risk and controlled release
  • Incident response, failover, RTO/RPO and recovery compatibility

Learn it in context

Reliable services & operational ownershipOwn the running service
Data, infrastructure & recoveryMeasure recovery and data-loss objectives

Course lessons use your existing training access. The practice workbook and curriculum map are free.

Evidence to bring to review

A user-centered SLO, bounded capacity plan, exact release evidence and an exercised recovery procedure.

Then change one condition

The old application is available but cannot read new records. Explain why reinstalling it is not safe recovery.

Run the reliability and queue exercise
10

Human–AI experience & useful adoption

Do people understand the system and actually benefit from it?

Design understandable proposals, confirmations and recovery. Test the critical task with people and accessible interaction, then measure total effort rather than generated activity.

Builds onBusiness discovery & problem framingModels, agents & workflow architectureEvaluation science & economic reasoning

Illustrative learning artifact

Make the next decision understandable

01ProposedInspect the exact change
02AuthorizedCheck current permission
03ConfirmedVerify the recorded result

Lost acknowledgement? Show “pending confirmation.”

What you need to understand

  • Mental models, evidence and human decision design
  • Accessible interaction and failure recovery
  • Pilot design, support and adoption measurement
  • Decision records and stakeholder communication

Learn it in context

Human-centered AI delivery & adoptionMake the human workflow usable and measurable
Contact-center AI foundationsOptional voice and conversational specialization

Course lessons use your existing training access. The practice workbook and curriculum map are free.

Evidence to bring to review

An observed task review, accessible recovery flow and pilot comparison that includes rework and receiving-team capacity.

Then change one condition

Drafting becomes faster but review becomes slower. Reassess the value claim and the rollout decision.

Use the task-review and pilot worksheets

Depth without losing the thread

Choose a specialization.
Keep the engineering core.

Build depth in one cloud, compare the boundaries in the others, and choose an industry context. Certification study complements the practical work; vendor exams and field acceptance are separate assessments.

Cloud & NVIDIA systemsGoogle Cloud · AWS · Azure · GPUs and private AITools & domain practiceClaude Code · Codex · LangGraph · MCP · healthcare · financial servicesReturn to first principlesPlain-language definitions, visual explanations and technical vocabulary
Why this curriculum is structured this way Research reviewed 2026-10-23

The route combines published FDE practice with cloud architecture, software delivery, reliability, AI risk, accessibility and economic reasoning. The lessons and exercises are original LockedIn Labs material. These references inform the curriculum; they do not imply vendor endorsement or accreditation.

Completion of reading or a synthetic lab does not establish field competence. The final review needs observed work, domain judgment and a receiving owner in the demonstrated environment.