AI SWE / Agentic SDLC Workflow Engineer for T Cloud Public (m/f/d)

Our brand Deutsche Telekom IT Solutions Slovakia entered the life of Košice region in 2006 under the name of T-Systems Slovakia and ever since has been inextricably linked with the region when became one of the founding members of Košice IT Valley. We have managed to grow from scratch to the second largest employer in the eastern part of the country with more than 3900 employees. Our goal is to proactively find new ways to improve and continuously transform into the type of company providing innovative information and communication technology services.

Mission

Industrialize AI-assisted engineering workflows for Meridian 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, OpenStack-derived services, CI/CD outputs, architecture documentation, and handover evidence.

Responsibilities

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 Chinese 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.; Have 5+ years in platform engineering, DevOps, 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 Terraform, Ansible, Helm, and GitOps workflows.; Have good understanding of networking, storage, access control, monitoring, and operational hardening in high-security environments.; Are comfortable working in sovereignty-driven environments where auditability, isolation, and controlled data handling are mandatory.

Requirements

Agentic workflow frameworks such as LangGraph, AutoGen, CrewAI, OpenAI Agents SDK, LlamaIndex Workflows, Semantic Kernel, or comparable orchestration stacks.; AI development platforms and editor integrations such as Cursor, Windsurf, Claude Code, Continue, Cline, Aider, or VS Code-compatible internal assistants.; Model families relevant to SDLC automation such as DeepSeek Coder, Qwen/Qwen-Coder, CodeGeeX, StarCoder, Code Llama, Mistral, or enterprise-hosted frontier models.; Supporting components including vector databases, graph stores, code indexing, OpenAPI wrappers, GitLab/GitHub APIs, Jenkins APIs, observability, and evaluation dashboards.; 5+ years of engineering experience across software development, DevOps automation, platform engineering, or AI workflow implementation.; Strong hands-on Python skills, API integration experience, and practical knowledge of orchestration frameworks, RAG patterns, tool calling, and evaluation loops.; Experience turning prototypes into reusable engineering workflows with clear interfaces, logging, error handling, configuration, and maintainability discipline.; Good understanding of CI/CD, Git workflows, containers, Kubernetes, software architecture documentation, and modular cloud software environments.; Strong written communication skills for creating workflow documentation, evidence packs, usage guidance, and decision support for senior stakeholders.; Comfortable operating in ambiguous, confidentiality-sensitive settings where AI outputs must be reviewed, justified, and converted into reliable engineering evidence.