AI SWE / Agentic SDLC Workflow Engineer

• 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. 

• 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. 

• International, dynamic and collaborative environment.
• T-Social: social initiatives (sports, community, health,...).
• Hybrid work model (remote/on-site).
• Flexible working hours.

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.