AI SWE / Agentic SDLC Workflow Engineer

• 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 thatremainauditable, 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.

Examples of market tools, models, and SDLC platforms expected

• Agentic workflow frameworks such asLangGraph,AutoGen,CrewAI, OpenAI Agents SDK,LlamaIndexWorkflows, 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,OpenAPIwrappers, 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.

T-Systems is part of the Deutsche Telekom Group, with around 30.000 employees worldwide. We create technology with purpose to generate a positive impact on society. We are looking for curious talent, eager to learn, take on challenges, and contribute ideas that transform our customers’ experience.

We trust people: we offer autonomy, continuous support, and a collaborative environment where you can grow without limits. We are one global team, guided by respect, integrity, and a passion for doing better every day.