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. 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.

Tailor and deploy agentic applications across verticals (e.g., 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.

Use AI coding assistants daily as a standard part of delivery, actively, frequently, and with demonstrable impact on productivity and output quality Apply AI across the full software delivery lifecycle: AI-generated tests, AI-assisted debugging,



AI-accelerated code review, and prompt engineering for development tasks Own the quality of AI-generated outputs in your delivery scope, exercise engineering judgment about reliability, limitations, and failure modes; know when AI output is production-ready and when it is not present AI productivity and quality metrics to project stakeholders Own delivery end-to-end — from design through to production support — in Agile sprint cycles alongside client engineering teams Build and integrate the application layers, APIs, and interfaces that connect full-stack systems to agentic backends — understanding data flows, context handoffs, and integration points between your code and AI pipelines Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field Comercial software engineering experience in production environments (or equivalent demonstrated through academic projects, internships, or shipped personal projects) Proficiency in at least one primary backend language: Python, Java, or TypeScript Basic understanding of web technologies including JavaScript, HTML, and CSS Familiarity with cloud fundamentals (AWS, Azure, or GCP), containers (Docker),



and CI/CD pipelines Understanding of Agile delivery fundamentals Experience with databases — SQL or NoSQL Familiarity with agentic system concepts — awareness of orchestration frameworks (LangChain, LangGraph, or equivalent), RAG pipelines, and how full-stack applications connect to agent-based architecture; 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.

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. Tailor and deploy agentic applications across verticals (e.g., 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.

📌 Software Ai Engineer - Remote (Madrid)
🏢 Accenture
📍 Madrid