4 publicaciones de empleo destacadas de observability engineer en madrid, md (¡Están contratando!)

Senior Specialist Digitalization Digital Plant (m/f/d) 1
BASF Digital Solutions S.L. —Madrid, Madrid provincia
- Azure
- Informática
- DevOps
10h
Senior Full-Stack .NET Engineer (with Node.js & Go)
GR8_TECH —Madrid, Madrid provincia
- Teletrabajo
- Desarrollo de software
- Liderazgo
Solicitud rápida
4h
Java Backend Developer
SIX Group AG —Madrid, Madrid provincia
- Teletrabajo
- Español
- DevOps
- Inglés
Software Engineering Team Lead
M-KOPA —Madrid, Madrid provincia
- Jornada completa
- Teletrabajo
- Azure
- DevOps
- AWS
3d

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Senior Specialist Digitalization Digital Plant (m/f/d) 1

BASF Digital Solutions S.L. -
Madrid, Madrid provincia

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Detalles del empleo

Contrato indefinido
Hace 10 horas

Cualificaciones

CI/CD
Azure
Derecho
Informática
Data lake
DevOps
Git
Máster
RBAC
Docker
PostgreSQL
Terraform
SDKs
Desarrollo de software
Unity
GitHub
APIs
Cloud
Inteligencia artificial
Python

Descripción completa del empleo

WELCOME TO BASF

Digitalization is a true part of BASF's DNA — creating new customer experiences, driving business growth, and making processes more efficient. Global Digital Services drives BASF's 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 BASF's 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 don't 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 DevHub's 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 — DevHub's 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

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