AI SDLC Lead Engineer (Cloud Architect) (m/f/d)

Odmeňovanie

From 2 200 EUR/month

Final salary is negotiable.

We are offering base salary depending on seniority level and previous experience of candidate. In addition to base salary we provide variable part and other financial benefits.

Lead AI-assisted software engineering activities by designing repeatable SDLC patterns that help analyse, validate, document, and evolve complex cloud software stacks. The role anchors the engineering approach for OpenStack-derived services, repository-scale understanding, and sovereign software handover readiness.

Role focus

This senior position focuses on turning AI development platforms into disciplined engineering capability. The candidate should guide how coding agents, open-source or open-weight coding models, and enterprise-approved toolchains are used to accelerate code understanding, refactoring support, documentation, and transition readiness without weakening evidence quality or confidentiality.

What will you do?

• Define and lead AI-assisted SDLC patterns for repository intake, code exploration, service mapping, build diagnosis, documentation, and engineering evidence generation.
• Guide analysis of OpenStack-derived control-plane services, APIs, dependencies, integration flows, and build structures across multiple repositories and teams.
• Evaluate and operationalize AI development platforms, coding agents, and approved open-weight or coding model stacks for secure enterprise use cases.
• Establish human-in-the-loop controls, quality gates, prompt libraries, reusable review patterns, and evidence standards for AI-generated engineering outputs.
• Coach software engineers and platform specialists on safe, effective use of AI tools while coordinating with architecture, security, DevOps, and testing teams.
• Produce senior stakeholder-ready artefacts including transition risks, service decomposition views, codebase maturity observations, and recommended engineering actions.

Examples of market tools, models, and SDLC platforms expected

• AI development environments such as coding assistants and extensions connected to enterprise-approved model endpoints.
• Open-source, open-weight, or coding-capable models operated through approved controls.
• Agentic and SDLC workflow components such as graph-based orchestration, agent SDKs, retrieval-augmented generation pipelines, evaluation loops, and structured tool-calling patterns.
• Engineering environments including enterprise source-code repositories, continuous integration and delivery systems, deployment automation, containerization, orchestration, private package registries, and terminal-native automation workflows.