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

AI Native Software Engineer
Accenture —Madrid, Madrid provincia
- Jornada completa
- Java
- Máster
- APIs
3d
Senior Data & Analytics Engineer (Europe, 100% remote)
LimeFlight —Madrid, Madrid provincia
- Jornada completa
- Teletrabajo
- Azure
- Inglés
- SQL
Solicitud rápida
11d
GCP Senior AI Engineer
BIP - Business Integration Partners —Madrid, Madrid provincia
- Jornada completa
- Español
- Informática
- Inglés
Fullstack Engineer
Destinus —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- DevOps
- Java
- AWS
Solicitud rápida
AI Full Stack Software Engineer
IQVIA —Madrid, Madrid provincia
- Azure
- Informática
- DevOps
Senior Software Engineer, Backend (Test Enablers)
Affirm —Madrid, Madrid provincia
- Teletrabajo
- Java
- Desarrollo de software
- Python
- Seguro oftalmológico
11d
Senior Software Engineer - Clickpipes (CDC/Streaming)
ClickHouse —Madrid, Madrid provincia
- De guardia
- Teletrabajo
- Azure
- AWS
Technical Services Engineer - Spanish Speaking
DeepHealth —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Español
- Azure
- Informática
Solicitud rápida
Software Dev Engineer Machine Learning
Amazon Spain Services, S.L.U. —Madrid, Madrid provincia
- Jornada completa
- Informática
- Java
- SQL
Senior Azure Cloud Engineer
SGS —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Azure
- Informática
- DevOps
Solicitud rápida
Databricks Platform Engineer (f/m/d)
Axpo Group —Madrid, Madrid provincia
- Trabajo híbrido
- Microsoft Access
- Azure
- Informática
Platform Software Engineer
Nexthink —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Español
- DevOps
- Inglés
- Vacaciones pagadas
Solicitud rápida
Software Engineer - Backend for Medical Applications
Siemens Healthineers —Getafe, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Azure
- Informática
- DevOps
.NET Fullstack Engineer (f/m/d)
Axpo Group —Madrid, Madrid provincia
- Trabajo híbrido
- Azure
- DevOps
- Inglés
Senior AI Fullstack Engineer – Cloud Applications
HiveMQ GmbH —Madrid, Madrid provincia
- Jornada completa
- Teletrabajo
- DevOps
- Inglés
- Máster
Solicitud rápida
Staff Software Engineer — DevPlatform
Duetto Research —Madrid, Madrid provincia
- Teletrabajo
- Java
- Máster
- Hotel
Solicitud rápida
9d
Director Cloud Engineering (Hybrid)
Colliers International EMEA —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Azure
- Informática
- PMP
Solicitud rápida
Software Engineering Team Lead
M-KOPA —Madrid, Madrid provincia
- Jornada completa
- Teletrabajo
- Azure
- DevOps
- AWS
4d
.NET Fullstack Engineer (f/m/d)
Axpo —Madrid, Madrid provincia
- Azure
- DevOps
- Inglés
Developer Advocate (Remote)
Percona —Madrid, Madrid provincia
- Jornada completa
- Teletrabajo
- Java
- JavaScript
- Inteligencia artificial
- Vacaciones pagadas

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AI Native Software Engineer

Accenture -
Madrid, Madrid provincia

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Detalles del empleo

Jornada completa
Hace 3 días

Cualificaciones

CI/CD
Derecho
Java
Máster
Arquitectura de microservicios
Docker
Terraform
SDKs
APIs
Cloud
Inteligencia artificial
Python
Debugging

Descripción completa del empleo

We are:

A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.

You are

An AI Native Engineer with a strong foundation in building cloud-native solutions and hands-on experience designing and deploying agentic systems, especially for enterprise environments. You’re a critical thinker who thrives in ambiguity, delivering concrete results by designing, building, and running AI agents that augment workflows and scale across modern infrastructure.

You'll shape how enterprises adopt AI-native engineering - either by leading complex agentic solutions and developing engineering talent, or by owning critical technical areas end-to-end as a senior IC

The Work

You’ll partner directly with client stakeholders — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be net-new platforms and systems that need to be stitched together in our clients’ environments alongside our ecosystem partners.

Agent Architecture & Engineering

Design and build enterprise-ready AI agents incorporating retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
Implement resilient, testable, and maintainable agentic workflows that can be iterated on quickly.

AI Platform Integration

Develop and/or extend abstraction layers across AI providers (Anthropic, Google, OpenAI, etc.) to enable seamless integration and multi-provider enablement.
Contribute to shared libraries, SDKs, and patterns that can be reused across clients.

Cloud-Native Engineering

Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability stacks to deliver scalable AI-native systems.
Own deployment, monitoring, and troubleshooting for your services in production.

Domain-Specific Workflows

Tailor and deploy agentic applications across verticals (e.g., finance, healthcare, retail), adapting to domain-specific processes and constraints.
Work closely with client SMEs to translate business workflows into agentic solutions.

Client Engagement

Participate in and/or lead design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders, fostering trust and adoption.
Communicate trade-offs, risks, and recommendations clearly to both technical and non-technical audiences.

Measure & Improve

Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.
Iterate rapidly based on data, feedback, and changing requirements.

Knowledge Sharing

Craft reusable patterns, documentation, and best practices that influence internal assets and client roadmaps.
Contribute to internal communities of practice around AI-native and agentic engineering.

Travel may be required for this role. The amount of travel will vary from 25% to 75% depending on business need and client requirements.

Key Responsibilities

Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability
Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets
Integrate and abstract across multiple LLM providers — OpenAI, Anthropic, Vertex AI, and open-source models — with fallback routing, token, cost, and latency management
Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring
Embed directly with client engineering teams to design, prototype, and deploy agentic solutions — workshops, proofs of concept, code-with sessions, and architecture walkthroughs
Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster
Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness; present findings and recommendations to client stakeholders in business terms

Basic Qualifications

Hands on experience in software engineering experience in production environments
Exposure / hands-on experience designing and deploying agentic AI solutions in a production environment — non-negotiable
Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level
Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs
RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering
LLMOps fundamentals: eval harness design, prompt versioning, and production observability
Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm)
Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience
Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure

#LI-EU

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observability engineer empleos en madrid, md

AI Native Software Engineer

Senior Data & Analytics Engineer (Europe, 100% remote)

GCP Senior AI Engineer

Fullstack Engineer

AI Full Stack Software Engineer

Senior Software Engineer, Backend (Test Enablers)

Senior Software Engineer - Clickpipes (CDC/Streaming)

Technical Services Engineer - Spanish Speaking

Software Dev Engineer Machine Learning

Senior Azure Cloud Engineer

Databricks Platform Engineer (f/m/d)

Platform Software Engineer

Software Engineer - Backend for Medical Applications

.NET Fullstack Engineer (f/m/d)

Senior AI Fullstack Engineer – Cloud Applications

Staff Software Engineer — DevPlatform

Director Cloud Engineering (Hybrid)

Software Engineering Team Lead

.NET Fullstack Engineer (f/m/d)

Developer Advocate (Remote)

Quiero recibir la alerta de empleo más reciente de observability engineer en madrid, md

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