We're looking for an AI Engineer to join our Connected Health team at Zimmer Biomet. This is a fully remote, UK-based job. You'll design, build, deploy and improve generative AI and data-driven products that raise the quality of care and quality of life for orthopaedic patients worldwide.

You'll join our AI & Data Science team, which works across three areas:

• Product development: We develop prediction and decision-support algorithms, using classical ML and generative AI, for our patient- and clinician-facing applications.
• Product analysis: We help our product teams understand how people use our products, so they can make better decisions.
• Platform development: We build tooling and reusable infrastructure that supports AI and data-driven product development across Zimmer Biomet. We design it to follow best practice for Software as a Medical Device (SaMD).
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We aim to keep improving, to communicate openly, and to enjoy ourselves along the way.

How You'll Create Impact

In this role you will work closely with machine learning scientists and application software engineers to bring advanced data insights to life. You'll build automation pipelines, deploy and serve both predictive models and generative AI systems, integrate third-party platforms and tooling, and design for observability and monitoring in production, whilst ensuring that we meet our regulatory commitments.

Our work covers classical ML on structured, sensor and imaging data, and generative AI: retrieval-augmented generation and agentic systems. Because we build medical devices in a regulated context, quality, traceability and responsible AI matter at every step. You'll help us productionise these systems against standards including the EU AI Act and FDA guidance on AI/ML-enabled medical devices.

You'll help shape our technical roadmap and influence how the Connected Health team moves forward with AI products.

Today, Our Tooling Revolves Around

• Azure ML and Azure DevOps for model orchestration, CI/CD and development;
• Python for data pipelines, machine learning pipelines and model serving;
• MLflow (or similar) for experiment tracking and model registry, with monitoring for drift and performance in production;
• LLM/GenAI tooling including foundation-model APIs, vector stores/retrieval, orchestration frameworks and evaluation harnesses;
• Pulumi for infrastructure as code;
• Occasionally C# to support integration between our internal systems.
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We also use modern developer tooling. Responsible use of AI coding assistants like Claude Code is part of how we work day-to-day.