Senior MLOps & Data Engineer
Location: Oxford Based (Hybrid)
Type: Permanent
Salary: -90,000 - -120,000 + Bonus + Equity + Benefits

The Opportunity
We''re supporting an innovative biotechnology organisation that is investing heavily in the next generation of data, machine learning and scientific computing capabilities.

As part of a growing technology team, you will play a key role in building the infrastructure that enables scientists, engineers and researchers to develop, deploy and scale machine learning solutions within a highly data-driven environment.

This is a hands-on position suited to an experienced engineer who enjoys solving complex technical challenges across cloud infrastructure, data platforms, workflow automation and machine learning operations. You will help transform research and analytical workflows into reliable, secure and scalable production systems.

Responsibilities

• Design and build scalable MLOps infrastructure to support model deployment, monitoring, retraining and lifecycle management.

• Productionise machine learning and scientific computing workflows using Python, container technologies and modern software engineering practices.

• Develop cloud-native data pipelines across AWS and GCP to support ingestion, transformation, storage and inference workloads.

• Build integrations between laboratory systems, operational platforms and cloud environments using APIs and event-driven architectures.

• Support the collection, processing and management of large-scale experimental and operational datasets.

• Establish best practices for model versioning, experiment tracking, reproducibility, observability and platform governance.

• Collaborate with scientific, engineering and operational teams to convert research code into reliable internal products and services.

• Contribute to the design of AI-driven workflow orchestration and intelligent automation solutions.

• Improve platform reliability, security, scalability and cost efficiency.

• Create and maintain technical documentation, standards and operational runbooks.

Required Experience

• Strong commercial experience in MLOps, machine learning platform engineering, data engineering or cloud infrastructure engineering.

• Advanced Python development experience within production environments.

• Strong experience with Docker, Kubernetes and CI/CD pipelines.

• Experience building and operating cloud-native platforms in AWS and/or GCP.

• Experience designing and supporting data pipelines within complex technical environments.

• Familiarity with workflow orchestration tools such as Airflow, Prefect or Dagster.

• Experience implementing monitoring, logging, observability and platform governance practices.

• Strong understanding of software engineering principles, testing and deployment best practices.

• Ability to work collaboratively with technical and non-technical stakeholders.

Desirable Experience

• Experience within life sciences, healthcare, biotechnology, research, scientific computing or regulated environments.

• Familiarity with laboratory information systems, data platforms or scientific software ecosystems.

• Exposure to AI agents, workflow automation frameworks or advanced machine learning operations.

• Experience supporting GPU-based workloads and large-scale model execution environments.

• Knowledge of compliance, auditability or data integrity requirements within highly regulated industries.

What''s on Offer

• Opportunity to help shape the architecture of a growing machine learning and data platform.

• High-impact role with significant technical ownership.

• Exposure to cloud infrastructure, machine learning, automation and scientific computing challenges.

• Flexible remote working environment.

• Long-term career growth within a rapidly evolving technology organisation.

If you are having difficulty in applying or if you have any questions, please contact Neil Walton @ (url removed)