Junior AI Engineer – Agentic SDLC Workflows for T Cloud Public (m/f/d)

Odmeňovanie

From 2 000 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.

Purpose

Mission

Industrialize AI-assisted engineering workflows by packaging repeatable patterns that support codebase intake, dependency extraction, build triage, documentation generation, and technical evidence creation across cloud software work packages.

Role focus

This position emphasizes reusable workflow engineering. The candidate should combine Python, APIs, orchestration frameworks, retrieval patterns, and AI development platforms to create governed workflows that can be reused across repositories, derived services, CI/CD outputs, architecture documentation, and handover evidence.

What will you do?

• Build reusable AI-assisted workflows for repository analysis, code scanning, service decomposition, dependency discovery, build diagnosis, and documentation generation.
• Package prompts, tools, retrieval layers, model routing, evaluation checks, retries, and human approval steps into repeatable engineering accelerators.
• Integrate AI workflows with Git platforms, CI/CD systems, documentation stores, issue trackers, test outputs, service catalogues, and architecture evidence repositories.
• Create workflow outputs that remain auditable, including traceable source references, confidence indicators, reviewer checkpoints, and explicit assumptions.
• Experiment with open-source, open-weight, and coding models in approved environments to compare usefulness for SDLC automation and handover tasks.
• Support work-package leads by translating ambiguous engineering questions into structured AI-assisted workflows and validated deliverables.

You will succeed if you:

• Have 5+ years in platform engineering, DevOps, Site Reliability Engineering (SRE), or MLOps, with strong Kubernetes and Linux expertise
• Have proven experience with AI infrastructure, model serving, private or on-prem deployments, and production operations for LLM-based workloads.
• Have strong hands-on skills in Python plus automation tooling such as infrastructure-as-code, configuration management, packaging tools, and GitOps workflows.
• Have good understanding of networking, storage, access control, monitoring, and operational hardening in high-security environments.
• Have API integration experience, and practical knowledge of orchestration frameworks, RAG patterns, tool calling, and evaluation loops.