Skip to content
LOCKEDIN LABS TRAINING · ROLE PATHS 04 ROLES
THE ROUTES · AUTHORED FACTS
Role paths published
4
Shared-core modules
3
Role practice modules
14
Role workshops
4
Engineering benchmarks
3
Standing the benchmarks target
L3

Counted from the curriculum in this repository, not authored by hand. A learning path organizes current lessons; it does not award a separate role certification.

ONE SHARED FOUNDATION

Learn the basics together. Apply them in your role.

A common language helps people make better decisions together. Start with how models work, write clear instructions, and design a useful workflow.

  1. 01 · SHARED CORE

    Thinking in models

  2. 02 · SHARED CORE

    Prompting as specification

  3. 03 · SHARED CORE

    Designing your first AI workflow

Lessons open in your signed-in workspace with your program access.

CHOOSE YOUR ROLE

Different responsibilities. One shared outcome.

Begin with a short exercise, then work through a deeper role assignment. Shared course reading supports the decisions you will make with the team.

01 · ROLE PRACTICE

Developer

Build useful AI features and show that they behave as intended.

You own: Implementation, tests, and technical tradeoffs.

Build the difficult case.

Create a behavior specification + failure test in a short guided exercise. No account required.

Continue with these lessons

  1. The AI-capable developer day
  2. Spec-driven development with agents
  3. Reviewing machine code
  4. Ship an agent to production

Bring it to the team

A working slice with acceptance results and known limits.

Have a teammate try a failing case. Explain the failure, revise the implementation, and rerun the check.

Keep your own evidence

Your implementation decisions, test results, and the change you made after review.

Try developer practice
02 · ROLE PRACTICE

Product manager

Find a valuable problem, test the assumptions, and define what good AI behavior means.

You own: Customer evidence, outcome decisions, and acceptance criteria.

Find the useful AI opportunity.

Create a opportunity brief + experiment in a short guided exercise. No account required.

Continue in your role

Supplied evidence, practical decisions, and a facilitator guide. Open in your signed-in workspace; no agent build is required.

  1. Choose the next product experiment.

    Read conflicting stakeholder evidence, calculate the real effort, and choose what deserves a bounded next test.

  2. Make a release decision from the evaluation evidence.

    Inspect supplied results, separate correctness from process, and ask engineering for the missing proof.

Supporting course reading

  1. Diagnose the work, establish the outcome
  2. Where AI actually pays
  3. Evaluating agent behavior

Bring it to the team

An outcome brief, a tested assumption, and examples of acceptable and unacceptable behavior.

Ask a teammate to challenge the value case. Revise the experiment and explain which customer evidence changed your decision.

Keep your own evidence

Your discovery record, experiment decision, and rationale for the acceptance criteria.

Try product manager practice
03 · ROLE PRACTICE

Project manager

Keep AI delivery coordinated, with clear decisions, dependencies, and accountable owners.

You own: Delivery cadence, dependencies, risks, and stakeholder communication.

Keep AI delivery moving responsibly.

Create a delivery plan + dependency decision in a short guided exercise. No account required.

Continue in your role

Supplied evidence, practical decisions, and a facilitator guide. Open in your signed-in workspace; no agent build is required.

  1. Replan a pilot from the evidence on the board.

    Separate commitments from estimates, trace real dependencies, and give the sponsor a plan the team can act on.

  2. Coordinate a recovery that someone can accept.

    Read an incident timeline, preserve uncertainty, and organize a recovery handoff without diagnosing beyond the evidence.

Supporting course reading

  1. The AI native SDLC
  2. Directing agentic delivery
  3. Operate adoption, rollout, and incidents

Bring it to the team

A delivery plan with decision owners, dependency checks, and an escalation path.

Run a short delivery review after a dependency slips. Agree the next decision, its owner, and how the team will verify recovery.

Keep your own evidence

Your delivery map, risk decisions, and revised plan after the team review.

Try project manager practice
04 · ROLE PRACTICE

Forward-deployed engineer

Work inside a customer's constraints and leave behind a system someone can operate.

You own: Customer discovery, integration, controlled rollout, and ownership transfer.

Leave a workflow someone can operate.

Create a pilot runbook + ownership handoff in a short guided exercise. No account required.

Continue with these lessons

  1. Diagnose the work, establish the outcome
  2. Scope and architect the mission
  3. Land inside enterprise systems
  4. Yield ownership and compound field learning

Bring it to the team

An integrated workflow, its operating limits, and a usable ownership handoff.

Ask a teammate to operate the workflow from your handoff. Observe where they get stuck and repair the instructions or system.

Keep your own evidence

Your mission decisions, integration evidence, and the changes prompted by the handoff rehearsal.

Try forward-deployed engineer practice
WORK TOGETHER · SHOW YOUR CONTRIBUTION

The team rehearses. Each person explains their decisions.

A learning pod can combine product, engineering, and delivery responsibilities around one problem. Discuss the tradeoffs, try the handoff, and revise the work after feedback. An individual learner can rehearse the same handoff with a peer or facilitator.

Cohort learning

Follow a shared cadence with practice, discussion, and a review. Live sessions and facilitation are agreed for the program.

Explore cohort formats

Certificates and specialist training

These learning paths organize current lessons; they do not award a separate role certification. LockedIn Labs credentials and external provider awards each retain their published requirements.

Understand the credentials
GO DEEPER IN ENGINEERING

Inspect the technical role benchmarks.

For engineering roles, compare the authored capability floors, evidence requirements, and the workplace experience each route still requires. The FDE delivery pod retains its specialist seat model.

Forward-deployed engineer

Works with a customer under real constraints to diagnose a workflow, deliver a credible technical wedge, and leave behind an operable system.

Primary evidence: The enterprise AI control plane

Inspect the benchmark

Full-stack AI developer

Builds and operates user-facing AI features across product, services, agent tooling, evaluation, and deployment boundaries.

Primary evidence: Ship an agent to production

Inspect the benchmark

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.

Primary evidence: Design an agent fleet

Inspect the benchmark