Senior MLops (Full Stack) Engineer | London | Foundation Models

Latchmere
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Data Scientist

Machine Learning Engineer Python AWS

Senior Data Scientist

Senior Machine Learning Engineer

Data Engineer

Senior MLops (Full Stack) Engineer | London | Foundation Models

What you’ll do
Build and maintain APIs (FastAPI or similar) to serve ML models
Design and manage robust ML infrastructure using Kubernetes, Docker, and Terraform
Deploy machine learning models into production and optimize them for performance
Collaborate with ML teams to streamline training, deployment, and monitoring
Build internal tools and dashboards (e.g., in React or Vue) for analytics and observability
Own CI/CD pipelines and drive infrastructure automationWhat you’ll bring
5+ years’ experience in backend or infrastructure-focused engineering roles
Strong Python and API development skills (FastAPI, Flask, etc.)
Proven experience with model deployment, containerization, and orchestration (K8s, Docker)
Infrastructure-as-code experience (Terraform, Helm, etc.)
Familiarity with cloud platforms like AWS, GCP, or Azure
Bonus: Frontend experience (React, Vue.js) for building internal tools
Why Join Us?

🌍 Tackle the biggest challenge in AI – Be part of the mission to bend the curve on compute costs, energy waste, and emissions in the LLM arms race. Our optimiser is redefining how the world trains and serves large models.
🧠 Work on the frontier – You’ll engineer the infrastructure behind cutting-edge AI systems — pushing the boundaries of speed, efficiency, and scale with a team that lives at the intersection of ML, systems, and optimisation research.
🚀 High-impact, high-autonomy – We’re a lean, expert-led team where your work ships fast, matters deeply, and scales globally. Expect ownership, speed, and the freedom to build without bureaucratic drag.
💥 Foundation model as infra – Our optimiser is itself a foundation model. You’ll help serve, adapt, and scale it in the wild — an opportunity few engineers will ever get.
💸 Equity that means something – You’re not late to the party. Join at a time when your equity still reflects the upside you help create.u

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.

Careers

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.

Jobs

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.

Careers Jobs

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.