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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 4 of 6

Deploy within boundaries

Make identity, security, infrastructure and operational responsibility explicit.

07

Security, privacy & AI governance

Who may do what, with which data, under whose accountability?

Treat identity, tenant isolation, data handling and tool authority as system properties. Connect threats and risks to implemented controls and evidence, with domain review where needed.

Builds onData foundations & trustworthy retrievalEnterprise integration & compatible changeModels, agents & workflow architecture

What you need to understand

  • Identity, least privilege, secrets and tenant isolation
  • Prompt injection, unsafe output and excessive agency
  • Threat modeling, retention, provenance and auditability
  • Risk ownership, approval and jurisdiction-specific review

Learn it in context

Build and operate enterprise AIProtect retrieval and tool execution
Build and operate enterprise AIConnect controls to accountable decisions

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

Evidence to bring to review

A threat-and-control map, negative authorization cases and an auditable explanation of permitted scope.

Then change one condition

Access is revoked between retrieval and execution. Demonstrate the current authority check rather than relying on an old prompt.

Study identity and permission boundaries
08

Cloud, compute & infrastructure delivery

Where will this run, and can you explain the responsibility you are taking on?

Build depth in one cloud and compare equivalent boundaries in the others. Learn GPU and private-inference economics when workload or data constraints justify that route.

Builds onSoftware engineering & the working environmentEnterprise integration & compatible changeSecurity, privacy & AI governance

What you need to understand

  • Accounts/projects/subscriptions, regions and zones
  • Networks, private compute, IAM and managed data
  • Terraform state, plan, drift and safe change
  • NVIDIA GPUs, memory, CUDA, NeMo, Nemotron and serving

Learn it in context

Google Cloud: infrastructure to AI deliveryGoogle Cloud specialization
AWS: infrastructure to agent deliveryAWS specialization
Azure: infrastructure to AI apps and agentsAzure specialization
NVIDIA AI systems: from GPU to productionGPU and private AI specialization
Data, infrastructure & recoveryVersion and review infrastructure changes

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

Evidence to bring to review

A placement and responsibility map, reviewed infrastructure plan and measured workload assumptions for the chosen environment.

Then change one condition

A region, quota or data-residency assumption changes. Revise the design and explain the operational and cost consequences.

Compare vendor study and certification routes

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.