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# AI SWE / Agentic SDLC Workflow Engineer

- **Company:** T-Systems Iberia
- **Location:** Spain
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Continuous Integration, Cursor (Graphical User Interface Elements), Decision Support Systems, Github, Python (Programming Language), Open Source Technology, Software Architecture, Systems Development Life Cycle, Software Engineering, Systems Integration, Data Logging, Gitlab, Git, Git Flow, Kubernetes, Deployment Automation, Automation Anywhere, Jenkins
- **Published:** August 15, 2026
- **Apply:** https://www.jobleads.com/es/job/e210154407e7201a83379ccddfb7558b4

## About the Role

* 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.

## Description

* 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.


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