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

SmartRecruiters, Inc.; Granada, Sevilla, Reus, Valencia; Híbrido; EUR 70.000 - 100.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid work model

Flexible working hours

T‑Social initiatives

Health insurance

Meal vouchers

Childcare support

Transportation aid

Social fund

Vacation 26+ days

Descripción de la vacante

T-Systems Iberia is seeking an AI SWE / Agentic SDLC Workflow Engineer to design and implement AI-assisted workflows for SDLC automation. You will build reusable pipelines, integrate with Git, CI/CD, and documentation stores, and evaluate multiple models and toolchains to improve engineering efficiency.

Role requires strong Python skills, API integration, and experience with orchestration frameworks. This hybrid role offers collaboration across global teams and a focus on maintainable, traceable

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

Python

API integration

Orchestration frameworks

RAG patterns

Tool calling

Evaluation loops

CI/CD

Git workflows

Kubernetes

Software architecture

Herramientas

LangGraph

AutoGen

CrewAI

Semantic Kernel

LlamaIndex Workflows

Descripción del empleo

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

• Full-time

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 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.

• 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.

Whatdoweofferyou?

• T‑Social: socialinitiatives(sports,community,health, ...).
• Hybridworkmodel(remote/on‑site).
• Flexibleworkinghours.

Growth&development

• Weeklylanguageclasses(English & German).
• InternationalMentoringSessions&ExperienceDays.
• Flexiblecompensationplan (healthinsurance,mealvouchers,childcare,transport).
• Socialfund.

Wellbeing& time off

• 26+workingdaysofvacationperyear.

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.