Senior Platform Engineer - AI MLOps Oxford, England, United Kingdom

Ellison Institute, LLC
Oxford
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Staff Data Engineer

Senior Data Engineer

SAS Data Engineer

Lead Data Engineer

The Ellison Institute of Technology (EIT) Oxford’s purpose is to have a global impact by fundamentally reimagining the way science and technology translate into end-to-end solutions and delivering these solutions in programmes and platforms that respond to humanity’s most challenging problems.


EIT Oxford will ensure scientific discoveries and pioneering science are turned into products for the benefit of society that can have high-impact worldwide and, over time, be commercialised to ensure long-term sustainability.


Led by a world-class faculty of scientists, technologists, policy makers, economists and entrepreneurs, the Ellison Institute of Technology aims to develop and deploy commercially sustainable solutions to solve some of humanity’s most enduring challenges. Our work is guided by four Humane Endeavours: Health, Medical Science & Generative Biology, Food Security & Sustainable Agriculture, Climate Change & Managing Atmospheric CO2 and Artificial Intelligence & Robotics.


Set for completion in 2027, the EIT Campus in Littlemore will include more than 300,000 sq ft of research laboratories, educational and gathering spaces. Fuelled by growing ambition and the strength of Oxford’s science ecosystem, EIT is now expanding its footprint to a 2 million sq ft Campus across the western part of The Oxford Science Park. Designed by Foster + Partners led by Lord Norman Foster, this will become a transformative workplace for up to 7,000 people, with autonomous laboratories, purpose-built laboratories including a plant sciences building and dynamic spaces to spark interdisciplinary collaboration.


Our MLOps team

Join ourMLOpsteam to build the cloud and compute foundation that enables scientific breakthroughs. Deliver reliable, secure platforms and self-service guardrails that accelerate experimentation and turn ideas into results—faster, at scale, and with confidence.


Day-to-day, you might:

  • Architect, build, andoperateour cloud platform, moving infrastructure beyond theinitialsetup to deliver resilient compute, network, and storage, including full-sized GPU clusters
  • Drive the implementation of highly structured, auditable delivery pipelines (CI/CD/GitOps) using to enforce automated, repeatable infrastructure changes
  • Design and deploy automated governance and security controls using Policy-as-Code (specificallyKyvernoand YAML) to ensure strong isolation, protect data, and meet internal audit standards
  • Establish the foundational monitoring, alerting, and telemetry frameworkrequiredfor robust operations, defining clear SLOs, and setting the course for future SRE work
  • Partner with Research and Data teams to build self-service capabilities that efficiently support diverse workloads, from Python notebooks to distributed clusters

What makes you a great fit:

  • Proven experience platform engineering, with a demonstrabletrack recordof architecting and automating operational processes
  • A highly proactive attitude and a passion for introducing and automating operational structure
  • Expertisewith at least one major cloud provider (OCI, AWS, GCP, or Azure)
  • Proficiencywith Terraform for declarative, large-scale infrastructure provisioning
  • Comfortable with operating and managing large-scale, resilient Kubernetes clusters
  • Proficiencyin at least one major language for system-level tools (e.g. Python, Go, or Java) with some scripting experience

It would also be great if you had:

  • Familiarity with modern Policy-as-Code tooling
  • A passion for introducing and automating operational rigour and structure
  • Experience supporting ML and Data Engineering workloads

We offer the following salary and benefits:

  • Enhanced holiday pay
  • Pension
  • Life Assurance
  • Income Protection
  • Private Medical Insurance
  • Hospital Cash Plan
  • Therapy Services
  • Perk Box
  • Electric Car Scheme

Why work for EIT:

At the Ellison Institute, we believe a collaborative, inclusive team is key to our success. We are building a supportive environment where creative risks are encouraged, and everyone feels heard. Valuing emotional intelligence, empathy, respect, and resilience, we encourage people to be curious and to have a shared commitment to excellence. Join us and make an impact!


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.