AI Engineering Workflow Developer (mf/d)

IGLUBIT

Sevilla

Presencial

EUR 42.000 - 64.000

Jornada completa

Hace 9 días

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Envía un currículum específico para el puesto de trabajo en cuestión de minutos.

Ventajas ofrecidas por este puesto de trabajo

Hybrid work model(remote/on-site)

Flexible working hours

Professional development

Descripción de la vacante

T-Systems Iberia is seeking a software-focused engineer to develop AI-assisted utilities that index repositories, summarize code, extract dependencies, parse logs, and generate documentation.

You will collaborate with senior engineers, configure AI environments, test models, and build lightweight automation services connecting Git, CI/CD, and issue trackers, while maintaining thorough documentation and quality practices.

Formación

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.

Responsabilidades

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.

Conocimientos

Python

APIs

Scripting

Automation

DevOps

Herramientas

Git

Docker

Kubernetes

CI/CD

Linux

Descripción del empleo

Company 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.

Job Description

Key responsibilities

Implement andmaintainsmall 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 tovalidateAI-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 escalat[e] 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

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