Senior MLOps Engineer

KDR Talent Solutions
Oxford
1 month ago
Applications closed

Related Jobs

View all jobs

Senior MLOps Engineer

Senior MLOps Engineer

Senior MLOps Engineer - Production ML at Scale

Senior MLOps Engineer - Scale & Automate ML Platforms

Senior MLOps Engineer: Scale AI Pipelines

Senior MLOps Engineer: GenAI & NLP Production Expert

4 days ago Be among the first 25 applicants


This range is provided by KDR Talent Solutions. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.


Base pay range

Direct message the job poster from KDR Talent Solutions


AI & Data Science Recruiter - Supporting companies in hiring the best professionals in across Data Science, AI and Machine Learning

Are you passionate about building cutting‑edge infrastructure that powers world‑class machine learning research? We’re partnering with a pioneering organisation at the forefront of science and technology to find a Senior MLOps Engineer who will play a key role in designing and operating high‑performance ML platforms.


The Opportunity

This is your chance to join a team that’s shaping the future of AI and scientific innovation. You’ll be part of a collaborative environment where experimentation is accelerated through robust, secure, and scalable infrastructure. If you thrive on solving complex challenges and want to make a global impact, this role is for you.


What You’ll Do

  • Design, build, and optimise GPU training and inference clusters for large‑scale ML workloads.
  • Implement high‑throughput data pipelines, focusing on I/O optimisation, caching, and data locality.
  • Drive performance benchmarking and resolve bottlenecks across compute, network, and orchestration layers.
  • Establish observability, resilience, and automated security controls for sensitive research environments.
  • Collaborate with research and data teams to forecast capacity and streamline ML experimentation pipelines.
  • Cloud Platforms: AWS, Azure, GCP (cloud‑agnostic approach).
  • Storage & Compute: Lustre, high‑throughput storage systems, GPU architecture, high‑speed networking.

What We’re Looking For

  • Proven experience designing and operating ML compute clusters at scale.
  • Strong knowledge of GPU architecture and distributed training environments.
  • Expertise in IaC and containerised systems.
  • Ability to work autonomously and collaboratively in a fast‑paced, innovative setting.

Why Apply?

  • Work on projects that have a global impact.
  • Join a culture that values creativity, collaboration, and continuous learning.
  • Enjoy a comprehensive benefits package including private medical insurance, enhanced holiday, pension, and more.

Ready to take the next step in your career? Apply today and be part of a team that’s redefining what’s possible in AI and scientific innovation. Send us your CV or get in touch to learn more – your future starts here!


Seniority level

Mid‑Senior level


Employment type

Full‑time


Job function

Information Technology


Industries

  • Software Development
  • Data Infrastructure and Analytics
  • IT System Custom Software Development

Referrals increase your chances of interviewing at KDR Talent Solutions by 2x


#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.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.