Senior MLOps & Data Engineer / Senior MLOps & Data Engineeress

Senior MLOps & Data Engineer

Location: Oxford Based (Hybrid)

Type: Permanent

Salary: £90,000 - £120,000 + Bonus + Equity + Benefits

The Opportunity

We are supporting an innovative biotechnology organization that is heavily investing 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 empowers scientists, engineers, and researchers to develop, deploy, and scale machine learning solutions within a highly data-driven environment.

This hands-on position is suited for an experienced engineer who enjoys tackling complex technical challenges across cloud infrastructure, data platforms, workflow automation, and machine learning operations. You will be instrumental in transforming 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.
• Productionize 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 organization.

Apply Now

If you are interested in this exciting opportunity, please apply online using the form below. Only applications matching the job profile will be considered.

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