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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Ai Platform Engineer , Madrid - **Company:** BASF SE - **Location:** Madrid, Spain - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, User Authentication, Microsoft Azure, Cloud Computing, Continuous Integration, DevOps, Github, Graph Database, Python (Programming Language), PostgreSQL, Open Web Application Security, Role-Based Access Control, Search Technologies, Software Engineering, GitHub Copilot, ReactJS, Large Language Models, Grafana, Multi-Agent Systems, Generative AI, Backend, Git, Fastapi, Containerization, AI Platforms, Core Data, Kubernetes, Information Technology, Non-relational Database, Machine Learning Operations, Front End Software Development, Terraform, Software Version Control, Dynatrace, Docker, Databricks, Web Api - **Published:** September 8, 2026 - **Apply:** https://www.buscojobs.com.es/ai-platform-engineer-m-f-d-madrid-en-madrid-ID-369400909 ## About the Role QUALIFICATIONS - BSc or MSc in Computer Science, Software Engineering, AI, or related field. - 4+ years in Software Engineering or Platform Development, with demonstrable recent experience in Generative AI and/or Agentic Systems. - You dont need to tick every box. Strong Python + hands-on LLM application experience + a platform/developer-experience mindset matter most we expect you to grow into the rest. - AI / LLM engineering. Practical experience building LLM-powered applications familiarity with RAG and agentic patterns (ReAct, plan-and-solve, multi-agent) and a clear sense of when not to use an autonomous agent. - Evaluation quality (core). Designing eval harnesses, golden tests and retrieval-quality metrics for LLM/RAG systems (grounding, retrieval precision, hallucination control) context engineering over model selection. - Backend API development. Python proficiency is highly desired (FastAPI, Pydantic, async) designing and operating production backend services and well-versioned APIs. - Platform / Developer-Experience engineering. Building reusable, self-service components and paved roads (templates, SDKs, golden paths) and operating multi-tenant services in production (SLOs, observability, you build it, you run it ). - Software development across the stack. Enough context across frontend, backend, and infrastructure to contribute across DevHubs stack (with AI-assisted coding) - no need to be a full-stack expert in every layer. - Cloud infrastructure. Hands-on Azure and containerization (Docker, Kubernetes/AKS) infrastructure-as-code (HCL/Terraform, modular). - Data state management. Relational/non-relational databases (PostgreSQL) and vector stores (e.G. Azure AI Search) managing context and state at scale. - DevOps production operations. CI/CD (Git, GitHub Actions), monitoring/observability, and security best practices in production. - AI / LLM ecosystem. LLM providers/APIs (OpenAI, Anthropic, Mistral), managed AI services (Azure AI Foundry, Databricks), and the MCP standard. (At DevHub, models are consumed through the internal AI Gateway, not provider SDKs directly.) - Security multi-tenancy. Authentication/authorization, RBAC, tenant isolation, guardrails, auditability awareness of the OWASP LLM Top 10. - Agentic standards beyond MCP (e.G. A2A) spec-driven ( spec-kit ) agentic development with AGENTS.Md / skill conventions. Nice to Have - Databricks / Unity Catalog - DevHubs core data platform DevHub governs the Databricks ## Description AI Platform Engineer (m/f/d)WELCOME TO BASF Digitalization is a true part of BASFs DNA - creating new customer experiences, driving business growth, and making processes more efficient.Global Digital Services drives BASFs digital transformation through innovative, global, high-quality digital products and a strong agile culture, and the Digital Hub Madrid is one of our key global delivery locations.We are seeking a hands-on AI Engineer for BASFs DevHub - the Internal Developer Platform (IDP) used by thousands of engineers and product teams across BASF.DevHub already ships an enterprise AI Gateway (50+ governed models, Entra ID, EU data residency, per-cost-center billing, Grafana observability) and a catalog that is a schema-validated knowledge graph of every product and its infrastructure.Your mission is to make AI a first-class platform capability: build reusable, production-grade AI services and developer experiences that help users discover, create, configure, operate, scale and govern their products, surfaced where they already work - the portal, the IDE (GitHub Copilot/MCP) and Teams.You will treat the platform as a product - shipping paved-road components other teams reuse, serving both humans and agents, with the multi-tenant scoping, cost-tracking, guardrails and governance an enterprise platform demands.RESPONSIBILITIES - Treat the platform as a product.Build paved roads and self-service: reusable AI building blocks (shared retrieval/ context engine, guardrail evaluation libraries, an MCP/tool layer), scaffolder templates, SDK/API access and stable, versioned interfaces - built once, reused across features.- Ship AI experiences that delight developers.Grounded, well-cited assistants, copilots and wizards across the product lifecycle (e.G. a conversational knowledge assistant over our docs and catalog), meeting users on the portal, IDE (Copilot/MCP) and Teams via one shared API.- Serve humans and agents.Expose platform capabilities through an MCP / SDK / API surface - read-first, RBAC- and tenant-aware - so internal and external AI clients can query and (later, gated) act on the platform.See the AI-Assisted Platform Strategy RFC.- Own evaluation and quality.Build eval harnesses, golden tests and retrieval-quality metrics so features are correct, grounded and regression-tested in CI invest in context engineering over model-shopping - the Gateway already solves model choice.- Pick the right pattern.Prefer deterministic pipelines + structured outputs + human-in-the-loop where outcomes are structured reserve multi-step/multi-agent orchestration (Azure AI Foundry Agent Service, LangGraph / Microsoft Agent Framework) for genuinely open-ended tasks, keeping state-changing actions gated.- Strengthen MLOps / LLMOps.Improve prompt/version management, model adaptation, CI/CD and the path from experiment to production treat prompts and retrieval as versioned, tested production assets.- Build for multi-tenancy.Default to per-product / per-tenant scoping of context, tools and actions bake in observability (OpenTelemetry, Grafana, distributed tracing) and per-product cost/FinOps visibility.- Help advance security, safety governance.Inherit platform RBAC (Entra ID / AccessIT), defend against the OWASP LLM Top 10, keep AI usage auditable, and respect BASF / EU AI Act and data-residency requirements.QUALIFICATIONS - BSc or MSc in Computer Science, Software Engineering, AI, or related field.- 4+ years in Software Engineering or Platform Development, with demonstrable recent experience in Generative AI and/or Agentic Systems.- You dont need to tick every box.Strong Python + hands-on LLM application experience + a platform/developer-experience mindset matter most we expect you to grow into the rest.- AI / LLM engineering.Practical experience building LLM-powered applications familiarity with RAG and agentic patterns (ReAct, plan-and-solve, multi-agent) and a clear sense of when not to use an autonomous agent.- Evaluation quality (core).Designing eval harnesses, golden tests and retrieval-quality metrics for LLM/RAG systems (grounding, retrieval precision, hallucination control) context engineering over model selection.- Backend API development.Python proficiency is highly desired (FastAPI, Pydantic, async) designing and operating production backend services and well-versioned APIs.- Platform / Developer-Experience engineering.Building reusable, self-service components and paved roads (templates, SDKs, golden paths) and operating multi-tenant services in production (SLOs, observability, you build it, you run it ).- Software development across the stack.Enough context across frontend, backend, and infrastructure to contribute across DevHubs stack (with AI-assisted coding) - no need to be a full-stack expert in every layer.- Cloud infrastructure.Hands-on Azure and containerization (Docker, Kubernetes/AKS) infrastructure-as-code (HCL/Terraform, modular).- Data state management.Relational/non-relational databases (PostgreSQL) and vector stores (e.G. Azure AI Search) managing context and state at scale.- DevOps production operations.CI/CD (Git, GitHub Actions), monitoring/observability, and security best practices in production.- AI / LLM ecosystem.LLM providers/APIs (OpenAI, Anthropic, Mistral), managed AI services (Azure AI Foundry, Databricks), and the MCP standard.(At DevHub, models are consumed through the internal AI Gateway, not provider SDKs directly.) - Security multi-tenancy.Authentication/authorization, RBAC, tenant isolation, guardrails, auditability awareness of the OWASP LLM Top 10.- Agentic standards beyond MCP (e.G. A2A) spec-driven ( spec-kit ) agentic development with AGENTS.Md / skill conventions.Nice to Have - Databricks / Unity Catalog - DevHubs core data platform DevHub governs the Databricks ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)