Senior MLOps & Data Engineer

This is a hands-on senior engineering role for someone who can take promising scientific and machine-learning workflows and translate them into robust, production-ready systems. You will work across cloud infrastructure, data engineering, model operations, and workflow automation to ensure that models, pipelines, and digital tools are reproducible, maintainable, and easy for scientific teams to use.

You will partner with computational scientists, software vendors, automation specialists, and laboratory teams to improve how data moves from instruments and LIMS platforms into secure cloud environments, and how models are packaged, deployed, monitored, and continuously improved. The ideal candidate is a strong systems builder who is comfortable operating in a fast-moving biotech environment.

• Design, build, and maintain scalable MLOps infrastructure for model deployment, monitoring, retraining, and lifecycle management.
• Product ionise Python-based scientific and machine-learning workflows using Docker, Kubernetes, and modern CI/CD practices.
• Build and optimise secure cloud-native pipelines across GCP and AWS for data ingestion, storage, processing, and inference.
• Develop robust integrations between laboratory systems, data platforms, and cloud environments, including APIs and event-driven workflows.
• Support integration of experimental and operational data from instruments, lab automation systems, and LIMS platforms into structured, analysis-ready datasets.
• Establish best practices for model versioning, experiment tracking, reproducibility, access control, auditability, and observability.
• Collaborate closely with computational biology and R&D teams to convert research code into reliable internal products and services.
• Help define architecture for agentic and workflow-driven AI systems that support scientific decision-making, orchestration, and data quality control.
• Contribute to technical documentation, platform standards, and engineering runbooks to support continuity, maintainability, and team scalability.
• Evaluate and improve platform performance, security posture, cost efficiency, and deployment reliability across the digital stack.

• 5+ years of experience in MLOps, data engineering, platform engineering, cloud infrastructure, or a closely related field.
• Strong Python engineering skills, including packaging, testing, and refactoring research or data-science code into production services.
• Deep experience with Docker and container orchestration, ideally Kubernetes.
• Strong hands-on experience with one or more major cloud platforms, with GCP and AWS strongly preferred.
• Experience building CI/CD pipelines and deployment workflows for machine-learning or data-intensive applications.
• Experience designing and consuming REST APIs and integrating heterogeneous data sources into governed pipelines.
• Familiarity with model monitoring, version control, experiment tracking, and reproducibility tooling such as MLflow, Weights & Biases, DVC, Kubeflow, or similar.
• Experience working cross-functionally with data scientists, domain experts, and operational teams to move models into production.
• Strong communication skills and the ability to translate technical constraints into practical delivery plans.

• Experience in biotech, life sciences, pharma, genomics, computational biology, or regulated R&D environments.
• Familiarity with laboratory data systems, LIMS platforms, or Benchling-style data models and APIs.
• Experience handling instrument-generated data, edge/IoT-style integrations, or automation-adjacent workflows.
• Exposure to agentic AI systems, workflow orchestration, or multi-agent frameworks.
• Familiarity with Airflow, Prefect, Dagster, or similar orchestration tools.
• Awareness of GxP, audit-trail, data-integrity, or compliance-oriented engineering practices in scientific settings.
• Experience supporting GPU workloads, batch compute, or high-performance model execution environments.

In this role, success means building a dependable digital platform that allows scientific and technical teams to work faster, with greater confidence in the underlying data and model operations. Within the first several months, the expectation is that key workflows are better structured, deployment pathways are clearer, cloud environments are better governed, and model execution becomes more repeatable and accessible across the organisation.

You should be excited by the opportunity to shape platform…