We are A forward-thinking services company at the forefront of AI-native innovation. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality. 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'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. 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 Cloud‐Native Engineering Leverage containerization (Kubernetes, Docker), microservices, serverless, event‐driven architectures, CI/CD, and observability stacks to deliver scalable AI‐native systems. Tailor and deploy agentic applications across verticals (e.Participate in and/or lead design workshops, POCs, and code‐with sessions to shape data‐driven agent workflows with stakeholders, fostering trust and adoption. 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. Contribute to internal communities of practice around AI‐native and agentic engineering.

Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks,



and ensure metric‐backed trade‐off 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‐levelAI impact in business terms to senior client stakeholders. Strong software engineering experience in production environments.

RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering. Cloud‐native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm).

Strong

Python; Java or equivalent backend language acceptable; Quality of experience is weighted over years; People lead responsibilities: experience managing, developing, and performance‐managing a team of engineers;

📌 Senior Engineering Architect​ (Madrid)
🏢 Accenture España
📍 Madrid