Senior MLOps & Data Engineer

In this senior role, you will design and operate scalable MLOps and data platforms to enable scientists and engineers to build, deploy, and scale ML and data workflows. You’ll work across cloud, data pipelines, and workflow automation to turn research code into reliable, governed production systems. This is a high-impact, hands-on position that shapes the architecture of a growing biotech data platform and drives efficiency, reliability, and collaboration across teams.

• Flexible remote working environment
• Bonus scheme
• Equity
• Comprehensive benefits

• Design and build scalable MLOps infrastructure for deployment, monitoring, retraining, and lifecycle management
• Productionise ML and scientific computing workflows using Python, containers, and modern software practices
• Develop cloud-native data pipelines across AWS and GCP for ingestion, transformation, storage and inference
• Integrate lab systems, operational platforms, and cloud environments via APIs and event-driven architectures
• Support collection and management of large-scale experimental and operational data
• Establish best practices for model versioning, experiment tracking, reproducibility, observability, and governance
• Collaborate with scientific, engineering and operational teams to turn research code into internal products and services
• Contribute to AI-driven workflow orchestration and intelligent automation solutions
• Improve platform reliability, security, scalability, and cost efficiency
• Create and maintain technical documentation, standards and runbooks

• Strong commercial experience in MLOps, ML platform engineering, data engineering or cloud infrastructure engineering
• Advanced Python development in production environments
• Experience with Docker, Kubernetes and CI/CD pipelines
• Cloud-native platform experience in AWS and/or GCP
• Experience designing and supporting data pipelines in complex environments
• Familiarity with Airflow, Prefect or Dagster
• Experience implementing monitoring, logging, observability and governance practices
• Strong understanding of software engineering principles, testing and deployment best practices
• Ability to work collaboratively with technical and non-technical stakeholders

• collaborative and cross-functional communication
• problem-solving and learning agility
• stakeholder management and teamwork
• Python (production-grade)
• Docker
• Kubernetes