Senior Applied AI Software Engineer (US hours)

Role Purpose

Building production systems powered by software engineering, data science and modern AI.

We are seeking a hands-on Senior Applied AI Software Engineer to design, build and deploy production systems that combine software engineering, data science, large language models (LLMs), agentic AI and intelligent automation. This is a software engineering role first and an AI role second. We are looking for someone who enjoys building real systems, shipping code and solving business problems, while applying modern AI technologies where they create meaningful value. You will work alongside Data Scientists, Engineers, IT teams and Business Analysts to transform ideas, models and business requirements into robust, scalable solutions. This includes operationalising statistical and decision-science models, building AI-powered products and workflows, selecting and integrating foundation models, optimising cost and performance, and developing agent-based systems that automate and enhance complex business processes. Initial focus areas include digital twin platforms, operational decision-support systems, AI-enabled research products, intelligent workflow automation, Copilot-style experiences and agentic AI solutions embedded across the organisation. Success in the role will be measured by the delivery of reliable, maintainable production systems that create measurable business value, rather than experimentation alone.

Key Responsibilities

Software Engineering & Product Delivery

• Design, build, test and maintain production software systems.
• Develop scalable services, APIs, integrations and automation workflows.
• Contribute to architecture, engineering standards and best practices.
• Support deployment, monitoring and continuous improvement of production solutions.

Applied AI & Intelligent Systems

• Design and deploy AI-enabled solutions using LLMs, agentic architectures and automation.
• Build production AI applications, copilots and workflow solutions.
• Develop prompt and context engineering frameworks.
• Implement RAG, tool-calling, memory and orchestration patterns.
• Evaluate and select appropriate AI models and technologies.
• Optimise latency, quality, reliability and cost.
• Monitor, test and continuously improve AI system performance.

Data & Decision Science Enablement

• Operationalise statistical, predictive and AI models.
• Build validation, monitoring and feedback mechanisms.
• Contribute to digital twin and decision-support platforms.

Business Collaboration & Technical Leadership

• Translate business requirements into scalable technical solutions.