20 publicaciones de empleo destacadas de java lead software en madrid, md (¡Están contratando!)

AI Native Software Engineer
Accenture —Madrid, Madrid provincia
- Jornada completa
- Java
- Máster
- APIs
2d
Senior / Expert AI Native Software Engineer
Accenture —Madrid, Madrid provincia
- Jornada completa
- Java
- Máster
- APIs
2d
Calypso Consultant Lead
Accenture —Madrid, Madrid provincia
- Jornada completa
- Java
- Grado
- Contabilidad
Software Engineering Manager
Pluxee —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Azure
- Informática
- DevOps
Lead Software Engineer, Madrid (M, ES)
Swiss Re —Madrid, Madrid provincia
- Jornada completa
- Oficina
- Java
- AWS
- Seguro de vida
Salesforce Tech Lead (Remote from Spain)
UST —Madrid, Madrid provincia
- Teletrabajo
- CRM software
- Azure
- Informática
- Abono gimnasio
Solicitud rápida
10d
Senior Software Development Engineer in Test
Solera —Madrid, Madrid provincia
- Jornada completa
- Informática
- DevOps
- Java
Senior Java Developer - Coaching & Delivery
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- Jornada completa
- Azure
- DevOps
- Inglés
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GMV —Tres Cantos, Madrid provincia
- Trabajo híbrido
- Informática
- Java
- JavaScript
- Seguro dental
Java Team Lead
EADTrust —Madrid, Madrid provincia
- Trabajo híbrido
- Azure
- Inglés
- Java
- Formación continua
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Software Development Manager
Solera —Madrid, Madrid provincia
- Jornada completa
- Oracle
- Java
- Desarrollo de software
Software Operations & Maintenance Lead
Minsait —Madrid, Madrid provincia
- Trabajo híbrido
- Oracle
- Inglés
- Java
Software Operations & Maintenance Lead
Indra —Madrid, Madrid provincia
- Trabajo híbrido
- Oracle
- Inglés
- Java
Expert Manager, Software Engineering (Full Stack)
Bain & Co. —Madrid, Madrid provincia
- Jornada completa
- Español
- Azure
- Informática
Solicitud rápida
Lead Software Systems Engineer - Technology Demonstrators
Minsait —Madrid, Madrid provincia
- Trabajo híbrido
- Java
- C++
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- Formación continua
- Ayuda al desarrollo profesional
Lead Software Test Automation Engineer
EPAM Systems, Inc. —Madrid, Madrid provincia
- Oficina
- Java
- C++
- Vacaciones pagadas
- Programa de recomendación
Java Technical Referent
dLocal —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Inglés
- Java
- AWS
Solicitud rápida
Solution Sales Specialist Apps
SoftwareONE —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Español
- Inglés
- Java
Control Centers Engineer rail signalling
Siemens —Tres Cantos, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Español
- Inglés
- Investigación
Test Lead
Sicpa —Madrid, Madrid provincia
- Informática
- Inglés
- Java

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AI Native Software Engineer

Accenture -
Madrid, Madrid provincia

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Detalles del empleo

Jornada completa
Hace 2 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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java lead software empleos en madrid, md

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Quiero recibir la alerta de empleo más reciente de java lead software en madrid, md

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