Verify software. Challenge AI. Prove what is safe to release.
Learn the craft of software testing, engineer reliable automation, verify APIs and integrations, evaluate RAG and AI-agent behaviour, test performance and security, and defend an independent release-risk recommendation with evidence.
A Quality Engineer produces the independent evidence that decides whether a system is safe to release — and states the risk that remains. On deterministic software that means tests against known expected outputs. On an AI-enabled system the output is a range, so the craft shifts from asserting one answer to characterising behaviour: coverage, invariants, abstention, drift and the conditions under which the system should refuse.
A Quality Engineer does not own the release decision — and must never quietly become the risk acceptor.
Quality describes the evidence and the residual risk in language a business owner can act on. The authorised product or risk owner makes the call. That separation is what makes the evidence worth anything.
Traditional software follows defined logic: input → known logic → expected output. AI-enabled systems add model, prompt, context, retrieval, tools and state — producing a range of possible behaviours. The testing craft remains essential, but exact expected outputs are no longer enough.
Trust nothing on demo. Verify everything on evidence.
The sequence is cumulative. Capstone risks, test data, traces and regression cases mature throughout the programme rather than appearing only in the final week.
Technical readiness, risk architecture and the testing craft that makes automation trustworthy.
UI, API, contract, integration and data-quality automation with diagnosable failure evidence.
Independent GenAI, RAG and agent evaluation, judge calibration and bounded red teaming.
Performance, resilience, cost, security, privacy and governance evidence.
Production feedback, supervision UAT and the defended release-risk recommendation.
Twelve modules. One independent evidence chain.
Establish the vocabulary, engineering fluency and reproducible workspace needed for modern software and AI quality work.
Turn requirements, workflows, architecture and failure impact into a prioritised evidence strategy.
Develop the human observation and test-design skills that make automation and AI assistance trustworthy.
Engineer reliable browser automation that tests user-visible behaviour and produces useful failure evidence.
Verify service behaviour and compatibility below the user interface, where many enterprise failures originate.
Verify behaviour across distributed services, asynchronous events, databases and imperfect environments.
Independently evaluate probabilistic language and retrieval systems using designed datasets, calibrated scoring and failure analysis.
Test an AI agent’s decisions, tools, state, permissions, approvals and trajectories — not only its final response.
Determine whether the system remains useful, reliable and economically viable under expected and adverse conditions.
Produce threat-informed evidence for application, API, data and AI-specific risk controls without presenting a test as a security guarantee.
Connect pre-release testing to production signals, human supervision, incident learning and an executive view of release risk.
Independently verify an integrated enterprise AI system and defend a release-risk recommendation under technical and business scrutiny.
TypeScript + Playwright for UI, Python + Pytest for API and evaluation, SQL for data. Selenium is taught through architecture, WebDriver concepts and migration exercises — beginners aren't asked to implement every lab twice.
You receive a production-shaped system and builder evidence from the Applied AI, Full Stack, Data and Operations tracks. The launch date is public, senior stakeholders want approval and the builder presents a high average score. You must determine whether the critical risk slices and human controls support release.
Test a fictional learner or customer portal using manual, exploratory, accessibility and defect-evidence methods.
Build a cross-browser regression suite with stable locators, controlled data, traces and CI.
Protect two services from a backward-incompatible API change.
Audit a knowledge assistant for retrieval, grounded answers, citations, permissions, freshness and unsupported questions.
Attack and evaluate a tool-using agent’s identity, permission, state and human-approval boundaries.
Model workload, measure percentile latency and cost per successful task, and test degraded dependencies.
Approved assistants may propose scenarios, draft automation, generate synthetic data, summarise logs, cluster defects and help draft evidence. You remain accountable for verifying expected results, checking assertions, reviewing generated code, detecting invented assumptions, testing negative paths and repairing the work live. AI can accelerate a Quality Engineer. It cannot accept your assessment responsibility.
Attendance, tool completion or a large number of passing checks is not sufficient. Test count, automation percentage and an AI score are never treated as proof of release safety.
Seven tracks each own a slice of the enterprise AI value chain. Each is individually employable and individually incomplete — enterprises don't fail at AI because a discipline is missing, they fail at the seams between them. The FDE is the single named human accountable for the whole outcome.
KDigital provides structured learning, assessed evidence, portfolio development and career-readiness support. A course cannot guarantee a job, salary, interview or promotion.
Professional testing experience, previous AI experience, advanced mathematics, model-building skill, cloud certification, a GPU and a specific framework are all not required.
The titles differ by employer — the role cluster above covers them. What does not differ is the ask: someone who can produce independent evidence about whether a system is safe to release, and defend it.
Product organisations where quality owns the release gate for platforms thousands of customers depend on.
Teams where the eval suite is the test suite, and non-determinism breaks every assumption a test framework makes.
Delivery practices supplying independent verification, where the evidence pack itself is the deliverable.
Regulated releases where independent challenge, traceability and residual-risk statements are mandatory.
Validated environments where test evidence is a regulatory artifact and abstention behaviour must be proven.
Groups standing up evaluation infrastructure, red-team practice and release policy across many agent products.
Categories reflect how these roles are commonly scoped and advertised across the market. Availability, titles and requirements differ by employer, region and year, and many roles expect prior industry experience — see the evidence methodology.
Individual outcomes reflect each graduate's prior experience and market conditions. The programme does not guarantee a role — see the evidence methodology.
Enterprise scale is where AI agents earn their keep — an agent plugged into the real service path, orchestrating work across teams as one fabric. That is the bar I teach to, every class.
Manikanta is the founder of KDigital and brings 15 years of enterprise platform architecture, where he led rollouts for Fortune-500 banks, telcos and insurers. Most recently he architected production agent deployments that replaced traditional triage tiers with governed, autonomous case handling.
His modules give you two things other programmes do not: a founding architect who has shipped enterprise AI from inside the Fortune 500, and a curriculum rewritten every release — so when interviewers ask about agent boundaries, injected failures or residual risk, you have already tested for them.
Operations is where AI stops being a demo and starts being the nervous system of the enterprise — telemetry you trust, alerting that is quiet on purpose, and agents resolving incidents before on-call gets paged.
Ravi is Chief Technologist at KDigital, where he leads the operations-engineering practice. After eight years building and running production DevOps pipelines, he stepped into the Chief Technologist seat to wire observability, evaluation and incident response into the way operations teams actually work.
His evaluation modules are built from real outage post-mortems, not slide decks. Expect to leave with a working eval suite, release-gate policy, red-team practice and an evidence pack a risk owner will sign.
A quality programme that made unverifiable claims about itself would be a poor advertisement for the discipline. No placement percentages, salary figures or borrowed employer logos appear on this page — ask us for anything on this list instead.
If the answer you need isn't here, book a 20-minute advisor call. No slides, no pitch — just your questions.
Campuses in Hyderabad and Bengaluru, plus live online cohorts running on Indian and US timezones.
Verify software, challenge AI, and graduate able to defend a release-risk recommendation under questioning from a release council.
Adjacent tracks that share modules, faculty and the same evidence standard.
Test non-deterministic systems: evaluation design, regression suites and release criteria for model-backed behaviour.
Level: Beginner to advanced. Delivery: Online, Blended, Onsite. Related roles: Quality Engineer, AI Evaluation Engineer, SDET. Credential: KDigital Academy programme completion credential, awarded on defended portfolio evidence.
Quality engineering is the discipline of discovering risk, designing evidence and communicating release confidence. Traditional QA emphasis was executing prescribed cases, testing after development and counting passed tests; quality engineering measures risk coverage, verifies UI, API, data, integration and AI behaviour, and communicates uncertainty and residual risk.
Yes, and it comes first. Modules 1–3 build observation, test-design and risk-analysis skill — including exploratory charters, boundary analysis, decision tables, state transitions and accessibility. Automation then scales meaningful checks rather than weak assertions.
Playwright Test with TypeScript is the core automation path, taught to framework depth with resilient locators, traces, CI and flaky-test diagnosis. Selenium WebDriver is taught through architecture, WebDriver concepts and migration/comparison exercises, because it still matters in enterprise estates.