Ai Platform Engineer , Madrid

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

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

Madrid, España

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.