> Markdown version of [/jobs/ext/2025152-ai-native-product-engineer-orchestrator](https://www.wearedevelopers.com/jobs/ext/2025152-ai-native-product-engineer-orchestrator). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Native Product Engineer & Orchestrator - **Company:** Accenture - **Location:** Madrid, Spain - **Salary:** €60,000.0 - €80,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Abstraction Layers, Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Cloud Engineering, Computer Programming, Continuous Integration, Software Debugging, Software Design Patterns, Python (Programming Language), Open Source Technology, Data Logging, Event Driven Architecture, Containerization, AI Platforms, Kubernetes, Virtual Agents, Terraform, Serverless Computing, Docker, Microservices - **Published:** August 11, 2026 - **Apply:** https://www.adzuna.es/contact-us.html ## About the Role + Strong engineering background. + Strong engineering experience with cloud-native systems (APIs, microservices, containerization, serverless). + Hands-on experience designing and deploying agentic AI solutions in production. + Solid experience with AI platforms (OpenAI, Claude, Vertex AI, open-source models), including multi-provider abstraction layers. + Strong expertise of programming experience in Python, Java, or equivalent. + Strong experience with CI/CD, infrastructure as code (Terraform, Helm), monitoring, logging, and debugging in production environments. + Proven experience leading client discussions, technical workshops, and delivery in ambiguous environments. Bonus Points If: + AI certifications or experience with advanced agentic tooling. + Prior experience as an Agentic AI Engineer in an enterprise setting. + Experience designing compound AI systems, orchestration frameworks, or agent registries. + Strong understanding of AI-native architecture principles, combining cloud-native and generative AI design patterns. + Experience delivering AI solutions across multiple industries. ## Description An AI Native Engineer with experience building cloud-native solutions, and deep expertise in designing and deploying agentic systems, especially for enterprise environments. You are a critical thinker that thrives in ambiguity, delivering concrete results by designing, building, and running custom AI agents that augment workflows and scale across modern infrastructure. You'll help shape the playbook for how enterprises adopt and scale AI-native engineering globally. The Work: You'll embed directly with clients - 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 completely net new platforms and systems that need to be stitched together in our clients' environments alongside our Ecosystem partners., + Agent Architecture and Engineering: Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability. + AI Platform Integration: Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc. ) to enable seamless integration and enablement. + Cloud-Native Engineering: Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability to deliver scalable AI-native systems. + Domain-Specific Workflows: Tailor and deploy agentic applications across verticals - e.g., finance, healthcare, retail - addressing domain-specific processes via intelligent automation. + Client Engagement: Conduct design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders, fostering trust and adoption. + Measure & Improve: Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness. + Knowledge Sharing: Craft reusable patterns, documentation, and best practices to influence internal assets and client roadmaps. Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure](https://www.wearedevelopers.com/videos/1532-agentic-ai-from-theory-to-practice-developing-multi-agent-ai-systems-on-azure) - [Microservices: how to get started with Spring Boot and Kubernetes](https://www.wearedevelopers.com/videos/242-microservices-how-to-get-started-with-spring-boot-and-kubernetes) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)