AI Engineering Workflow Developer (mf/d)

Aimlroles

La Coruña

Hybrid

EUR 42,000 - 66,000

Full time

14 days+

Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

Benefits offered by this job

Health insurance

Meal vouchers

Childcare

Transport

Telemedicine

Life and accident insurance

Social fund

Job summary

T-Systems Iberia in Spain is seeking a curious software engineering professional to join our AI tooling team. You will build AI-assisted utilities, integrate development tools, and help shape how we prototype and evaluate LLM-based solutions.

You will collaborate with senior engineers, work across repositories and CI/CD logs, and contribute to open-source and enterprise-grade workflows while growing your expertise in AI coding assistants and RAG-style development.

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 upscale 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.

Responsibilities

Implement and maintain AI-assisted utilities for repository indexing, code summarization, dependency extraction, and logging.
Assist senior engineers in configuring AI development environments, testing prompts, comparing model outputs, and documenting SDLC patterns.
Create scripts and lightweight services connecting Git repositories, CI/CD logs, issue trackers, documentation stores, and model endpoints.
Help prepare handover materials including codebase summaries, service notes, build observations, glossary entries, and templates.
Test open-source, open-weight, and Chinese models in approved environments and document strengths, risks, and guidance.
Participate in reviews to validate AI outputs, correct inaccuracies, and improve workflow quality.

Skills

Python

APIs

Scripting

Developer tooling

Documentation

LLM APIs

Git

CI/CD

Docker

Kubernetes basics

Tools

Python

FastAPI

LangChain

LlamaIndex

GitLab/GitHub APIs

Jenkins APIs

Markdown

Documentation automation

VS Code

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

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.

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 upscale 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.

Whatdoweofferyou?

Workenvironment&flexibility