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Academy

Learning to ship.

Codecta Academy trains engineers in-house, taught by the people who ship our products, on real systems rather than exercises. All three tracks teach traditional software engineering with a focus on AI — the fundamentals, then the part of the job that is now done alongside a model. Programmes are fully sponsored — free to the people on them — and they are also how we hire.

Programmes

Three tracks, one focus.

QA

Test automation engineer for AI-era systems

Classical test engineering, extended to software that includes a model: you learn to verify the system and to verify what the AI produced.

  • Test engineering fundamentals on the ISTQB syllabus — risk, coverage, and what is worth automating at all
  • Agile and Scrum in practice
  • Exploratory testing, and the cases where a person still finds what a suite cannot
  • Automated suites at API and UI level, built to survive a redesign
  • Testing non-deterministic systems — software that does not give the same answer twice
  • Evals for LLM features: datasets, scoring, and regression gates in CI
  • Reviewing AI-generated code and tests — how they fail, and how to catch it

Full-Stack

AI-native full-stack engineer

Traditional software engineering with a focus on AI: the same fundamentals, taught for engineers who build with AI and build AI into what they ship.

  • Shipping a feature end to end: data model, API, interface, deploy
  • The fundamentals that do not change — modelling data, designing APIs, and writing code someone else can own
  • Web and mobile interfaces, and the tradeoffs between building one of each and building both
  • Working with AI coding agents: prompting, reviewing, and being accountable for what they write
  • Building LLM features into a product — retrieval, prompts, evals, and a cost model that holds at real traffic
  • Containers and cloud, so your own work reaches production
  • Security and defensive programming, prompt injection and data leakage included
  • Twelve-factor apps, TDD and agile delivery

DevOps

DevOps engineer for AI systems

The platform side of the same shift: pipelines and environments that put code and models into production, and keep both accountable once they are there.

  • Linux, deeply — the layer every abstraction above it leaks through
  • Containers, Kubernetes and cloud administration
  • Infrastructure as code, and environments that rebuild the same way twice
  • CI/CD pipelines and automation, with evals as a release gate
  • Running AI services in production: inference endpoints, model and prompt versioning, rollback
  • Observability and cost control for AI workloads — latency, token spend, failure modes
  • Security and defensive programming

Who it is for

Open to graduates and to third, fourth and fifth-year IT students. The QA programme has run with no prior knowledge required, and no track assumes prior AI experience — that part is taught. The strongest students are offered on-the-job training and a full-time role at the end.

Next intake

Programmes have run in partnership with the BH Futures Foundation. Ask us about the next intake.

academy@codecta.com