6 publicaciones de empleo destacadas de cloud observability engineer en md (¡Están contratando!)

AI Architect Engineer
Accenture —Madrid, Madrid provincia
- Jornada completa
- Java
- Máster
- APIs
Data Architect
Roche —Madrid, Madrid provincia
- Jornada completa
- Azure
- Máster
- AWS
Senior Solutions Architect (Data & AI)
Kyndryl —Madrid, Madrid provincia
- Jornada completa
- Azure
- AWS
- APIs
Platform Architect
dLocal —Madrid, Madrid provincia
- Jornada completa
- Trabajo híbrido
- Informática
- DevOps
- Inglés
Solicitud rápida
2d
Platform Engineer
Backbase —Madrid, Madrid provincia
- De guardia
- Inglés
- Desarrollo de software
- Python
Solicitud rápida
Technical Lead - Core Applications - London or Madrid
Sonnedix —Madrid, Madrid provincia
- Jornada completa
- Informática
- Inglés
- APIs
2h

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AI Architect Engineer

Accenture -
Madrid, Madrid provincia

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Detalles del empleo

Jornada completa

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.

Job Description
Job Description
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.
Job Qualifications
Job Qualifications
Key Responsibilities
- Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, and programme-level lifecycle observability
- Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable
- Set multi-LLM integration standards: vendor-agnostic architecture by default, fallback routing and cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models
- Own LLMOps at programme scale: eval strategy, prompt governance, observability tooling standards, safety monitoring and cost controls across multiple concurrent systems
- Lead client engineering engagements at senior level — facilitate architecture design sessions, lead proof-of-concept delivery, and drive alignment between client technology leadership and delivery teams
- Shape and publish reusable patterns, accelerators, and engineering standards that scale across the practice and reduce ramp-up time on new client engagements
- Own the measurement framework for agentic system quality: define accuracy, latency, safety, and cost metrics; present programme-level AI impact in business terms to senior client stakeholders
Basic Qualifications
- Strong software engineering experience in production environments
- 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
- People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversation
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