Implement and maintain small AI-assisted engineering utilities for repository indexing code summarization dependency extraction log parsing and documentation generation.
Support senior engineers in configuring AI development environments testing prompts comparing model outputs and documenting repeatable SDLC usage patterns.
Create scripts and lightweight services that connect Git repositories CI/CD logs issue trackers documentation stores and internal model endpoints.
Help prepare handover materials including codebase summaries service notes build observations glossary entries and structured evidence templates.
Test open-source open-weight and Chinese coding models in approved environments and document strengths limitations risks and practical usage guidance.
Participate in reviews with senior engineers to validate AI-generated outputs correct inaccuracies and improve workflow quality over time.
Examples of market tools models and SDLC platforms expected
AI development environments such as Cursor Windsurf Continue Cline Aider Claude Code or VS Code-based extensions configured for enterprise repositories.
Model families used for coding support such as DeepSeek Coder Qwen/Qwen-Coder CodeGeeX StarCoder Code Llama Mistral or other internally approved models.
Workflow and integration tooling such as Python FastAPI notebooks LangChain LlamaIndex GitLab/GitHub APIs Jenkins APIs Markdown and documentation automation.
Supporting engineering tools such as Git Docker Kubernetes basics Helm basics Linux shells package managers log processing and structured prompt repositories.
Qualifications :
2-4 years in software engineering DevOps automation data engineering AI tooling or platform-adjacent development roles.
Good Python skills and willingness to work across APIs scripting developer tooling documentation tests and lightweight automation services.
Hands-on familiarity with AI coding assistants prompt engineering LLM APIs local model experimentation or RAG-style development is strongly preferred.
Basic understanding of Git CI/CD Linux containers cloud platforms and software architecture documentation with readiness to deepen OpenStack knowledge.
Careful working style with good documentation habits curiosity and ability to escalate unclear findings instead of over-trusting AI-generated answers.
Comfortable working in a confidential enterprise environment where learning speed quality discipline and structured communication are important.
Additional Information :
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.
Remote Work :
No
Employment Type :
Full-time
Experience: years Vacancy: 1
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AI Engineering Workflow Developer (mfd) • Granada, Andalucía, Spain