🇪🇸 Madrid, Spain
AI Platform Engineer (m/f/d)
BASF
TecnoempleomidVisa sponsorEnglish-friendlyonsite
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 account and environments (workspaces Unity Catalog blueprint lifecycle) not the Enterprise Data Lake or data distribution. Familiarity is a strong plus. - TypeScript alongside Python Grafana MLflow and data pipelines. - FinOps / cost attribution for AI features. - Message queues / event-driven data-intensive architectures (e.g. RabbitMQ Azure Event Grid). - Workflow engines / state machines OpenTelemetry distributed tracing. Experience with EnvoyProxy WHAT WE OFFER - A secure work environment because your health safety and wellbeing is always our top priority. - Flexible work schedule and Home-office options so that you can balance your working life and private life. - Learning and development opportunities - 25 holiday days per year - 5 additional days (readjustment) - A collaborative trustful and innovative work environment - Being part of an international team and work in global projects - Relocation assistance to Madrid provided At BASF the chemistry is right Because we are counting on innovative solutions sustainable actions connected thinking and on you become a part of our formula for success and develop the future with us - in a global team that embraces diversity and equal opportunities irrespective of gender age origin sexual orientation disability or belief.At BASF we are committed to upholding and ensuring compliance with company standards related to quality environment health safety and energy in line with our global guidelines.We actively promote a culture of prevention and continuous improvement encouraging collaboration in initiatives related to quality environmental protection health safety and energy performance.We foster responsible energy use promoting efficiency in daily operations and supporting the identification of improvement projects and energy-saving opportunities Apply now Madrid ESP BASF Digital Solutions S.L. Digitalization Permanent 141842 IT and Digitalization Spain Work modelSourced from Tecnoempleo. Relocantly aggregates public job postings; apply on the original site.