AI Native Software Engineer, Madrid
Empresa
Accenture
Provincia
Madrid
Ciudad
Madrid
Tipo de Contrato
Tiempo Completo
Descripción
AI Native Software Engineer
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. Youre 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.
Youll 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
Youll partner directly with client stakeholders - acting as both technologist and trusted advisor. Youll 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
Python, LangGraph, CrewAI, AutoGen, LLM,
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. Youre 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.
Youll 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
Youll partner directly with client stakeholders - acting as both technologist and trusted advisor. Youll 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
Python, LangGraph, CrewAI, AutoGen, LLM,