Software Engineer (Core AI Services)

Join AVEVA's Core AI Services team as a Software Engineer. In this role, you will design, develop, and scale AI-enabling platform services and public APIs that are secure, reliable, and cloud-native. You will work in an agile Scrum team, building scalable, fault-tolerant cloud-native services on Microsoft Azure, developing secure public APIs and SDKs, and collaborating with cross-functional teams. This position offers a hybrid working model, generous time off, and numerous benefits including private healthcare, gym membership, and professional development opportunities.

Design, develop, and scale AI-enabling platform services and public APIs that are secure, reliable, and cloud-native.

Responsibilities

• Collaborate with cross-functional teams to deliver end-to-end solutions across data pipelines, orchestration, and service APIs.
• Participate in design reviews, code reviews, and implementation discussions to ensure engineering excellence.
• If you are a motivated engineer who is energized by the challenge of building scalable platform services and clean APIs for modern AI workloads, we want to talk to you
• Working at the forefront of agentic systems and harness engineering, you will join an agile Scrum team with a true builder mindset—delivering innovative, secure, and production-grade AI capabilities with the agility of a startup
• Solid understanding of RESTful API design, versioning, testing, and lifecycle management

Requirements

• Minimum of 2 years professional experience in software engineering, preferably in platform or cloud-native service development, Microsoft and .NET technologies a plus
• Practical experience deploying and debugging scalable cloud-native services using PaaS, Docker, and Kubernetes
• Proficient in securing APIs, managing authentication/authorization and data privacy practices
• Excellent problem-solving skills, with the ability to analyse complex technical challenges and propose scalable solutions
• Interest or exposure to Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agent orchestration, prompt engineering, and tool calling
• Familiarity with AI standards such as Model Context Protocol (MCP) and Agent2Agent (A2A)
• Experience with tools for automated testing and evaluation of AI outputs
• Experience working in Agile teams and collaborating across global R&D locations