Flagship programme · Admissions open 12 weekends · Live cohort + four-week capstone · 10–14 hrs / week Explore the complete page
FLAGSHIP PROGRAMME · QA, DEVOPS & SRE

AI-Driven
QA, DevOps & SRE
Certification

Connect QA, DevOps and SRE into one production discipline—using automation and AI assistance without giving up control, evidence or reliability.

Across 12 weekends, you move through guided foundations, applied builds, review checkpoints and a tested, observable and recoverable delivery platform with measurable reliability objectives.

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

See whether this learning path fits your current profile.

Share four details. We’ll capture your interest and unlock the full programme plan for review.

Admissions open

ai-driven-qa-devops-sre-certification.plan12 weekends learning path
Learning path12 weekendsVisible evidence
Applied work18Visible evidence
Portfolio gates7Visible evidence
CapstoneIncludedVisible evidence

✓ understand the problem

✓ build the workflow

✓ evaluate the output

✓ explain the evidence

12live weekends
24guided labs
18reliability builds
7portfolio reviews
1platform 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-Driven QA, DevOps & SRE.

01

The tools are not the outcome

AI-Driven QA, DevOps & SRE is designed around decisions, repeatable practice and working artifacts—not passive tool coverage.

Reward: you can explain how modern test engineering, delivery automation, cloud operations, observability and reliability 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.

Modern test engineering proof01

Risk-Based Test Strategy

A portfolio-ready modern test engineering artifact with decisions, outputs and review evidence.

  • Working artifact
  • Decision notes
  • Review evidence
Automation frameworks proof02

Automation Framework

A portfolio-ready automation frameworks artifact with decisions, outputs and review evidence.

CI/CD & release proof03

Gated Delivery Pipeline

A portfolio-ready ci/cd & release artifact with decisions, outputs and review evidence.

Cloud & infrastructure proof04

Infrastructure-as-Code Environment

A portfolio-ready cloud & infrastructure artifact with decisions, outputs and review evidence.

Observability & reliability proof05

SLO and Alerting Pack

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

AI-assisted operations proof06

AI-Assisted Incident Runbook

A portfolio-ready ai-assisted operations 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.

MODERN TEST ENGINEERING

Foundations turns knowledge into the next level of applied evidence.

Learn the concepts, tools and decisions behind modern test engineering, then apply them to a reviewed artifact within AI-Driven QA, DevOps & SRE. The phase is paced to make each artifact useful to the phase that follows.

Your phase rewardA portfolio-ready modern test engineering artifact with decisions, outputs and review evidence.

Representative builds

  1. 01Risk-Based Test Strategy
  2. 02Automation Framework
  3. 03Gated Delivery Pipeline
  4. 04Infrastructure-as-Code Environment
  5. 05SLO and Alerting Pack
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.

MODERN TEST ENGINEERING

Build working capability in modern test engineering, not only vocabulary.

Learn the concepts, tools and decisions behind modern test engineering, then apply them to a reviewed artifact within AI-Driven QA, DevOps & SRE.

Modern test engineeringPlaywrightSeleniumGitHub ActionsDockerApplied projectReview evidence
You will be able to showA portfolio-ready modern test engineering 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
deployment: "green"
error_budget: "nearly exhausted"
rollback_test: "not run"
release_pressure: "high"
What would you do next?
Choose an action to see the programme rationale.
PlaywrightSeleniumGitHub ActionsDockerKubernetesTerraform
PlaywrightSeleniumGitHub ActionsDockerKubernetesTerraformPrometheusGrafanaModern test engineeringAutomation frameworksPlaywrightSeleniumGitHub ActionsDockerKubernetesTerraformPrometheusGrafanaModern test engineeringAutomation frameworks
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-Driven QA, DevOps & SRE.

24applied cases
3checkpoints
6capability levels
1final defence
1
Modern test engineeringWeeks 1–3 · applied checkpoint
Foundation
2
Automation frameworksWeeks 1–3 · applied checkpoint
Core pattern
3
CI/CD & releaseWeeks 4–6 · applied checkpoint
Integration
4
Cloud & infrastructureWeeks 4–6 · applied checkpoint
Evaluation
5
Observability & reliabilityWeeks 7–9 · applied checkpoint
Portfolio
6
AI-assisted operationsWeeks 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

Risk-Based Test Strategy

A reviewed risk-based test strategy that connects modern test engineering, delivery automation, cloud operations, observability and reliability 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.

QA Automation EngineerDevOps EngineerSite Reliability EngineerQuality Platform EngineerAI-Ready Production 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 modern test engineering.

02

Add applied credibility

Publish reviewed work across automation frameworks and ci/cd & release.

03

Practise the explanation

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

04

Defend the capstone

Present a tested, observable and recoverable delivery platform with measurable reliability objectives, 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 QA engineers, Automation engineers, DevOps engineers, SRE professionals. The exact prerequisites can later be governed through the headless CMS.

You are likely a strong fit if…

  • You want structured capability in modern test engineering, delivery automation, cloud operations, observability and reliability.
  • 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 Automation Framework 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-Driven QA, DevOps & SRE, 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 Reliable Delivery 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-Driven QA, DevOps & SRE.

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 QA engineers, Automation engineers, DevOps engineers, SRE professionals. The strongest fit is someone who wants applied capability in modern test engineering, delivery automation, cloud operations, observability and reliability 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 tested, observable and recoverable delivery platform with measurable reliability objectives.

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-Driven QA, DevOps & SRE 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
1platform capstone
Where should we send the prospectus?

Complete the short form and the PDF unlocks immediately.