AI SWE / Agentic SDLC Workflow Engineer (mf/d/)

T-Systems Iberia; Comunidad Valenciana; On-site; EUR 60,000 - 90,000; Full time

Benefits offered by this job

Coursera training access

Language classes (English & German)

Life and health insurance

Social fund

Mentoring sessions

Job summary

T-Systems Iberia in Valencia seeks a senior AI/DevOps engineer to design reusable AI-assisted workflows for SDLC automation, including repository analysis, code scanning, and documentation generation.

You will package prompts and tools, integrate with Git platforms and CI/CD, ensure auditable outputs, and evaluate open-source models in approved environments. Collaboration with leads and stakeholders is essential, with emphasis on structured workflows and reliable engineering evidence.

Qualifications

• 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.

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.

Skills

Python

API integration

Orchestration frameworks

RAG patterns

Tool calling

Evaluation loops

Tools

LangGraph

AutoGen

OpenAI Agents SDK

LlamaIndex Workflows

Semantic Kernel

Job description

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.

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.

Qualifications

• 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, ...).
• Customized training: access to Coursera to learn whatever you want, whenever you want.
• Weekly language classes (English & German).
• International Mentoring Sessions & Experience Days.
• 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!

T-Systems Iberia will only process the CVs of candidates who meet the requirements specified for each offer.