Senior MLOps & Data Engineer

Proclinical Staffing; Oxford; Hybrid; GBP 90,000 - 120,000; Full time

Benefits offered by this job

Bonus

Equity

Benefits

Job summary

Proclinical Staffing in Oxford is seeking a Senior MLOps & Data Engineer for a permanent hybrid role. You will design and implement scalable MLOps infrastructure to support model deployment, monitoring and lifecycle management across AWS/GCP.

You will productionise ML workflows in Python, build data pipelines, integrate lab systems and ensure observability, governance and cost efficiency for a data-driven biotech environment.

Qualifications

• Strong commercial experience in MLOps, machine learning platform engineering or cloud infrastructure engineering.
• Advanced Python development experience in production environments.
• Experience with Docker, Kubernetes and CI/CD pipelines.
• Experience building and operating cloud-native platforms in AWS and/or GCP.
• Experience designing data pipelines in complex environments.
• Familiarity with workflow orchestration tools (Airflow, Prefect or Dagster).
• Experience with monitoring, logging, observability and governance practices.
• Strong software engineering principles and deployment practices.
• Ability to work with technical and non-technical stakeholders.
• Experience in life sciences, biotechnology or regulated environments.
• Exposure to AI agents or workflow automation frameworks.
• Experience supporting GPU-based workloads.

Responsibilities

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

Skills

MLOps

Python

Docker

Kubernetes

CI/CD

AWS/GCP

Data pipelines

Observability

Collaboration

GPU workloads

Tools

Airflow

Dagster

Prefect

Job description

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.
• 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.