Applied AI engineer
Turns a business workflow into a measurable AI system, then owns the evaluation, safety boundaries, and iteration needed to make it useful.
Also observed as · AI engineer · applied ML engineer · agent engineer
This is a training route, not an employer screen, placement promise, or claim about your current readiness. A pilot request enters manual consideration only; it creates no invitation, consent, enrollment, reviewer assignment, or payment.
- Target standing
- L3
- Dependency gate
- 18 modules
- Authored module time
- Role proof
- Design an agent fleet
Module time excludes project build, review, and revision time.
A role target with a visible entry and a fixed bar.
The published route begins at L0 with Thinking in models and carries every prerequisite through the L3 target. The assessment can change where you begin; it does not change the proof required.
No module credit is assumed in this public route. Bring prior evidence to the assessment; qualifying reviewed work is the basis for any individual capability claim.
AI literacy & judgment
Explains model behavior, failure modes, and economic tradeoffs in terms a delivery team can act on.
Prompting & instruction design
Authors instruction and tool interfaces that can be inspected, versioned, and improved from evidence.
Workflow decomposition
Scopes the first useful workflow slice, identifies a human handoff, and names the decision that makes it valuable.
Agent direction & oversight
Builds orchestration around tools, state, and recovery instead of presenting a chat wrapper as a system.
Verification & quality control
Uses task-specific evaluation, adversarial cases, and release gates to determine whether the system earns use.
Security & governance awareness
Maps risk tier, data access, and escalation policy before an agent receives real authority.
Every prerequisite. In learning order.
18 authored modules across 4 tracks. The 12 hr 51 min total is the curriculum estimate, not a promise of elapsed time to capability.
- FND2 hr 59 min
- AI Native Foundations
- 5 modules
- DEV3 hr 42 min
- AI Native Developers
- 5 modules
- AGI3 hr 48 min
- Agentic Workflows & AI Agents
- 5 modules
- SDL2 hr 22 min
- AI Native SDLC & Control Plane
- 3 modules
- 01 · L0Thinking in modelsFND · Calibration log: 3 prompt variants, drift analysis, and your trust rules.27 MIN
- 02 · L0The AI native tool stackFND · One-screen map of your AI stack with job labels and two identified gaps.33 MIN
- 03 · L0Prompting as specificationFND · Before/after prompt pair with constraint analysis on three test inputs.38 MIN
- 04 · L1Designing your first AI workflowFND · Workflow definition + two run logs + checkpoint analysis.43 MIN
- 05 · L1Your personal AI operating systemFND · One-page personal AI OS + three days of retro notes.38 MIN
- 06 · L2The AI native dev environmentDEV · AGENTS.md + agent-produced diff + failure analysis.37 MIN
- 07 · L2Spec-driven development with agentsDEV · Executable spec + agent plan + green diff.47 MIN
- 08 · L2Anatomy of an agentAGI · Minimal agent config + annotated execution trace.31 MIN
- 09 · L2Tools, MCP, and protocolsAGI · Tool contract v1→v2 with documented agent failure.36 MIN
- 10 · L2The AI native SDLCSDL · Eight-phase delivery map for a real agent workstream + gate definitions + accountability labels.46 MIN
- 11 · L2Risk tiers and the control matrixSDL · Risk-tier control matrix for five real systems + the design change classification forced.41 MIN
- 12 · L3Test-driven agentic codingDEV · TDD transcript with mutation-test evidence.41 MIN
- 13 · L3Reviewing machine codeDEV · Risk-tiered review of an agent PR with demanded changes.36 MIN
- 14 · L3Orchestrating multi-step agentsAGI · Three-agent orchestration + run evidence from both gates.51 MIN
- 15 · L3Evaluating agent behaviorAGI · Three-layer eval suite + adversarial fix.48 MIN
- 16 · L3Ship an agent to productionDEV · Working agent + consequence/coverage/variance release evidence + redacted event contract + measured cost sheet + exercised containment and rollback runbook.61 MIN
- 17 · L3Design an agent fleetAGI · Fleet architecture + working two-agent slice with evals.ROLE PROOF62 MIN
- 18 · L3The verifier layerSDL · Six-class verifier suite wired into a pipeline + signed results for one real change + an induced failure.55 MIN
Design an agent fleet
Fleet architecture + working two-agent slice with evals.
- NODE
- n-l3-6
- STANDING
- L3
- ALIGNMENT
- AINO-A · Certified Agentic Architect
Completion alone is not verification. Only qualifying reviewed work can support the evidence record; credential requirements remain mechanically derived from the Capability Graph.
A production-shaped agent workflow
It exposes a real task boundary, tools, state ownership, failure handling, and a human escalation point.
Produced through
An evaluation and failure-analysis pack
A reviewer can rerun representative cases and see how the owner treats regressions and unsafe output.
Produced through
A delivery brief tied to a workflow metric
The brief names the user, decision, success measure, risk tier, and first release boundary.
Produced through
Published availability, without a sales fiction.
These are the current Repo-backed delivery formats aligned to the role proof track. A schedule or staffing commitment exists only when its published record says so.
Agentic Workflows — Intensive 11
- Schedule
- Schedule follows the founding cohort
- Cadence
- Daily labs · 3 weeks
Operator Lab 02 — In Person
- Schedule
- Scheduled when the venue is confirmed
- Cadence
- 3 days · in person
The route names what it does not create.
Customer-domain discovery and the political work of winning production access are not simulated by the current curriculum.
These are authored role targets already carried by the LockedIn Labs curriculum Repo—not job listings, a labor forecast, an employer screen, or a placement promise. They are not a measurement of any learner. Dimension floors describe the target; only qualifying reviewed work can support an individual capability claim.
The scan did not establish which framework, cloud provider, or degree requirement is universal across applied AI engineering roles.
Test the operating standard before choosing the full route.
The public Operator Trial compresses one synthetic release decision into about ten minutes. It creates no learner score, record, or hiring signal.