Key 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.
Examples of market tools, models, and SDLC platforms expected
- 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.
What do we offer you?
Work environment & flexibility
- International, dynamic and collaborative environment.
- T-Social: social initiatives (sports, community, health, ...).
- Hybrid work model (remote/on-site).
- Flexible working hours.
Growth & development
- Customized training: access to Coursera to learn whatever you want, whenever you want.
- Weekly language classes (English & German).
- International Mentoring Sessions & Experience Days.
Compensation & benefits
- Flexible compensation plan (health insurance, meal vouchers, childcare, transport).
- Telemedicine.
- Life and accident insurance.
- Social fund.
Wellbeing & time off
- 26+ working days of vacation per year.
- Free access to specialist services (medical, legal, wellness).
- 100% salary coverage during medical leave.
And many more advantages of being part of T-Systems!
If you are looking for a new challenge, do not hesitate to send us your CV! Please send CV in English. Join our team!
T-Systems Iberia will only process the CVs of candidates who meet the requirements specified for each offer.
