Flagship programme · Admissions open 12 weekends · Live cohort + four-week capstone · 10–14 hrs / week Explore the complete page
FLAGSHIP PROGRAMME · SOFTWARE ENGINEERING

AI-Native
Developer
Certification

Move beyond assisted coding and learn to design, build, evaluate and operate AI-native applications that behave reliably in production.

Across 12 weekends, you move through guided foundations, applied builds, review checkpoints and a production-ready AI application with retrieval, evaluation, guardrails and operational evidence.

Live cohort + four-week capstone10–14 hrs / weekCareer track from W7
Flagship programmeSample content is ready for later headless-CMS replacement
60-SECOND PROGRAMME FIT CHECK

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Admissions open

ai-native-developer-certification.plan12 weekends learning path
Learning path12 weekendsVisible evidence
Applied work20Visible evidence
Portfolio gates7Visible evidence
CapstoneIncludedVisible evidence

✓ understand the problem

✓ build the workflow

✓ evaluate the output

✓ explain the evidence

12live weekends
24guided labs
20applied builds
7portfolio reviews
1AI product capstone
Livecareer support
WHAT MAKES THIS PATH DIFFERENT

Coverage is useful. Transferable capability is the real objective.

This page uses the complete programme architecture of the Forward Deployed Engineer reference while adapting every section to AI-Native Developer.

01

The tools are not the outcome

AI-Native Developer is designed around decisions, repeatable practice and working artifacts—not passive tool coverage.

Reward: you can explain how reliable AI-native application delivery from model interface to production observability creates useful work.
02

Exercises must connect into a system

Individual tasks are linked so research, building, testing and review become one coherent delivery path.

Reward: a connected portfolio rather than disconnected tutorial output.
03

AI output needs verification

Plausible output is treated as a starting point. You practise checking sources, assumptions, quality, risk and edge cases.

Reward: evidence that you can use AI without outsourcing judgment.
04

Capability must survive explanation

Every milestone includes the reasoning behind the result: what changed, what failed, what was measured and what you would improve.

Reward: an interview- and stakeholder-ready account of your work.
WHAT YOU WILL HAVE TO SHOW

By the end, your progress should be visible in the work.

Not only a list of topics covered. A chain of artifacts showing how you understand, build, evaluate, improve and communicate.

AI application architecture proof01

AI Application Architecture Brief

A portfolio-ready ai application architecture artifact with decisions, outputs and review evidence.

  • Working artifact
  • Decision notes
  • Review evidence
LLM APIs & orchestration proof02

Structured Model Gateway

A portfolio-ready llm apis & orchestration artifact with decisions, outputs and review evidence.

Retrieval & vector systems proof03

Retrieval-Augmented Feature

A portfolio-ready retrieval & vector systems artifact with decisions, outputs and review evidence.

Evaluation & safety proof04

Evaluation Harness

A portfolio-ready evaluation & safety artifact with decisions, outputs and review evidence.

Production delivery & observability proof05

Guardrailed Production Service

A portfolio-ready production delivery & observability artifact with decisions, outputs and review evidence.

AI product capstone proof06

Observability Dashboard

A portfolio-ready ai product capstone artifact with decisions, outputs and review evidence.

THE 12 WEEKENDS JOURNEY

Capability compounds from foundations to a defensible capstone.

Four phases connect guided learning, applied work, evaluation and portfolio evidence without turning the page into an unstructured module list.

AI APPLICATION ARCHITECTURE

Foundations turns knowledge into the next level of applied evidence.

Learn the concepts, tools and decisions behind ai application architecture, then apply them to a reviewed artifact within AI-Native Developer. The phase is paced to make each artifact useful to the phase that follows.

Your phase rewardA portfolio-ready ai application architecture artifact with decisions, outputs and review evidence.

Representative builds

  1. 01AI Application Architecture Brief
  2. 02Structured Model Gateway
  3. 03Retrieval-Augmented Feature
  4. 04Evaluation Harness
  5. 05Guardrailed Production Service
HOW THE LEARNING RHYTHM WORKS

A predictable cadence for work that becomes progressively more complex.

Preparation creates fluency. Live sessions focus on implementation and judgment. Review turns activity into evidence.

“The objective is not to watch an expert work. It is to build, explain and improve your own artifact.”
FOUNDATION10–14 hrs / week

Guided preparation

  • Concept notes and short demonstrations
  • Tool setup and sandbox practice
  • Readiness checks before applied work
  • Documented questions and assumptions
  • Small exercises that feed the live build
LIVE STUDIOBuild together

Implementation Studio

Readiness & blockersClear setup and conceptual gaps.

Live implementationBuild from an empty state, decision by decision.

Learner build sprintExtend the artifact with guided support.

Failure drillDiagnose an intentionally broken output.

REVIEW LABEvidence first

Portfolio & Decision Studio

Reasoning clinicExplain choices and trade-offs.

Integrated buildConnect the week’s second artifact.

Judgment labRespond to ambiguity, quality or risk.

Review & next gateCapture feedback and the next improvement.

CURRICULUM EXPLORER

The curriculum is broad enough to connect the work—and specific enough to review.

Each track arrives when the next artifact needs it. Use the tabs to inspect all six capability layers.

AI APPLICATION ARCHITECTURE

Build working capability in ai application architecture, not only vocabulary.

Learn the concepts, tools and decisions behind ai application architecture, then apply them to a reviewed artifact within AI-Native Developer.

AI application architecturePython or TypeScriptLLM APIsVector databasesGitHubApplied projectReview evidence
You will be able to showA portfolio-ready ai application architecture artifact with decisions, outputs and review evidence.
Need the complete programme map?Review every phase, milestone and assessment gate.
JUDGMENT, NOT JUST TOOL FLUENCY

The work is applied. The differentiator is what you do when the output is incomplete.

Decision Labs create controlled ambiguity around quality, evidence, scope, risk and stakeholder pressure—so the programme tests reasoning as well as execution.

01

Applied decision labs across the learning path.

02

Deliberately imperfect inputs that require verification and trade-offs.

03

Recorded reasoning explaining what you accepted, rejected and changed.

04

AI disclosure notes separating assistance from verified human judgment.

LAB

Programme Decision LabRespond to the pressure—not only the request.

illustrative
Applied scenario
model_output: "correct in demo"
evaluation_coverage: "18%"
fallback_path: "missing"
release_request: "ship to users"
What would you do next?
Choose an action to see the programme rationale.
Python or TypeScriptLLM APIsVector databasesGitHubCloudDocker
Python or TypeScriptLLM APIsVector databasesGitHubCloudDockerObservabilityAI application architectureLLM APIs & orchestrationRetrieval & vector systemsPython or TypeScriptLLM APIsVector databasesGitHubCloudDockerObservabilityAI application architectureLLM APIs & orchestrationRetrieval & vector systems
ROLE-ALIGNED READINESS TRACK

Preparation follows the work—not a generic checklist.

The readiness path moves from fundamentals through applied scenarios, portfolio explanation and a final panel-style review aligned to AI-Native Developer.

24applied cases
3checkpoints
6capability levels
1final defence
1
AI application architectureWeeks 1–3 · applied checkpoint
Foundation
2
LLM APIs & orchestrationWeeks 1–3 · applied checkpoint
Core pattern
3
Retrieval & vector systemsWeeks 4–6 · applied checkpoint
Integration
4
Evaluation & safetyWeeks 4–6 · applied checkpoint
Evaluation
5
Production delivery & observabilityWeeks 7–9 · applied checkpoint
Portfolio
6
AI product capstoneWeeks 7–9 · applied checkpoint
Panel review
reasoning-mode
const review = {
  goal: "show reliable capability",
  ask: [
    "what evidence supports this?",
    "what failed and changed?",
    "what would you do next?"
  ]
};

status: "ready to reason aloud"
THE MILESTONE LADDER

Seven portfolio gates. Each one makes the capability easier to see.

Applied work happens throughout the programme. These milestone artifacts receive deeper review and become the strongest evidence in your final story.

01
Milestone 1 · W2

AI Application Architecture Brief

A reviewed ai application architecture brief that connects reliable AI-native application delivery from model interface to production observability to a visible decision, working output and evidence trail.

Portfolio valueFoundation proof
CAREER & APPLICATION TRACK

Career activity begins before the capstone is complete.

Role mapping, portfolio positioning, project explanation and structured practice are attached to the evidence as it appears—not postponed until the final week.

AI Software DeveloperAI Application DeveloperAI Product EngineerAI API & Integration EngineerFull-Stack Engineer

No job guarantee. Support improves evidence, readiness and application quality; outcomes depend on demonstrated capability, prior experience, interview performance and market conditions.

01

Build the foundation

Clarify the target roles and establish the first evidence around ai application architecture.

02

Add applied credibility

Publish reviewed work across llm apis & orchestration and retrieval & vector systems.

03

Practise the explanation

Turn project decisions, trade-offs and improvements into interview- and stakeholder-ready narratives.

04

Defend the capstone

Present a production-ready AI application with retrieval, evaluation, guardrails and operational evidence, receive critique and convert the result into a focused next-step plan.

ENTRY & FIT CHECK

This programme is designed for deliberate practice—not passive consumption.

The intended audience includes Developers, Software engineers, Full-stack engineers. The exact prerequisites can later be governed through the headless CMS.

You are likely a strong fit if…

  • You want structured capability in reliable AI-native application delivery from model interface to production observability.
  • You can protect the weekly effort shown on this page.
  • You are willing to build, revise and explain applied work.
  • You want portfolio evidence rather than passive completion.

This is probably not the right fit if…

  • You want only a list of tools or recorded lectures.
  • You cannot allocate time for applied work and review.
  • You prefer to accept AI output without verification.
  • You need a guaranteed employment outcome.
70minimum overall score
All core milestones submittedCapstone review completedAttendance and participation threshold
EVIDENCE-BASED CERTIFICATION

The capstone carries weight because the work should.

There is no single memory-based final exam. Certification is assembled from applied work, review quality, evaluation discipline, explanation and the final capstone.

25%Applied builds
20%Milestone reviews
20%Evaluation & judgment
15%Portfolio explanation
20%Capstone defence

These are sample assessment weights for the current static build. Replace them with the approved programme policy when the CMS is connected.

LEARNER TESTIMONIALS

Short learner stories, kept in motion.

Every card is intentionally marked as a sample placeholder. Replace it with a verified learner name, role, photograph and approved quote through the future CMS.

01
Verified learner storyProgramme learner
Sample

Replace this card with an approved quote about building Structured Model Gateway and the capability the learner can now demonstrate.

02
Verified learner storyWorking professional
Sample

Use this placeholder for a verified outcome-backed story connected to AI-Native Developer, without making an unverified placement promise.

03
Verified learner storyCareer-transition learner
Sample

Add an approved learner photograph, designation and concise quote linking the learning experience to AI Product Capstone.

THE FULL PROGRAMME GUIDE

Review the complete learning journey before you decide.

Explore the phases, curriculum tracks, milestone artifacts, learning rhythm, application support, fit criteria and assessment approach for AI-Native Developer.

Full curriculum mapSeven milestone artifactsAssessment framework
The admissions team can share the current programme guide after the short form is completed.
QUESTIONS, ANSWERED

Decide with the constraints visible.

A serious programme page should be explicit about audience, effort, applied work, certification and the limits of career support.

Who is this programme designed for?

It is designed for Developers, Software engineers, Full-stack engineers. The strongest fit is someone who wants applied capability in reliable AI-native application delivery from model interface to production observability and can protect the stated weekly effort.

Is this suitable for complete beginners?

The page includes a guided foundation, but the exact prerequisite depends on the programme. Use the fit check so the admissions conversation starts with your current experience.

How much time should I commit each week?

Plan for approximately 10–14 hrs / week. Applied builds and the capstone may require additional time during milestone weeks.

What is the learning format?

The current structure is Live cohort + four-week capstone. Guided preparation, live implementation, review and portfolio work are connected through the same milestone path.

Will I build projects?

Yes. The page includes applied work throughout and culminates in a production-ready AI application with retrieval, evaluation, guardrails and operational evidence.

How are AI tools used?

AI tools are used as accelerators, not as substitutes for judgment. Learners are expected to verify outputs, document assumptions and explain what they accepted, rejected or changed.

When does career or application support begin?

The current pathway includes career track from w7. Support is attached to portfolio evidence as it becomes available.

Does the programme guarantee a job?

No. Impacteers can support portfolio evidence, application readiness and interview practice, but employment depends on capability, prior experience, interview performance and market conditions.

How is certification awarded?

Certification is based on applied builds, milestone reviews, evaluation quality, portfolio explanation and a final capstone review. The published policy should be updated through the CMS once finalised.

Can the curriculum change?

Yes. Tools and examples may evolve as the field changes. The intended outcomes, evidence gates and approved programme policies should remain version-controlled in the future CMS.

BUILD. EVALUATE. EXPLAIN.

Ready to see whether AI-Native Developer fits your next move?

Review the complete programme guide or submit the fit check. Both paths are designed to make the decision more deliberate.

Impacteers PROGRAMME PROSPECTUS

Get the complete 43-page programme prospectus.

See the 24-week curriculum, 40 projects, technology stack, Field Labs, placement track, simulated client engagement and assessment criteria.

  • Full sprint-by-sprint specification
  • Seven portfolio milestones
  • Certification thresholds
1AI product capstone
Where should we send the prospectus?

Complete the short form and the PDF unlocks immediately.