Senior MLOps Engineer, UK

F-Prime Capital; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Proximie is hiring a Senior MLOps Engineer to own the production-ready platform and infrastructure for machine learning solutions in healthcare. You will deploy and operate training, processing and inference workloads on AWS SageMaker, ensuring reproducible pipelines and robust monitoring across global deployments.

The role emphasizes production-grade MLOps, data drift monitoring, and scalable deployment practices in a hybrid London-based environment.

Qualifications

• Expert Python with deep PyTorch experience and the surrounding ecosystem (Transformers, timm, scikit-learn).
• Production experience on AWS, specifically SageMaker for training and processing jobs.
• Practical MLOps: experiment tracking with MLflow and reproducible, configuration-driven training runs.
• Containerisation and infrastructure as code with Docker and Terraform; GPU image management in production.
• Strong software engineering fundamentals: Git, PR workflow, automated testing, CI/CD.
• Ability to explain technical trade-offs clearly to non-technical stakeholders.

Responsibilities

• Take models from ML engineers and turn them into production services meeting latency, cost and reliability targets.
• Build and maintain SageMaker training, processing and inference workloads and pipelines.
• Make training runs reproducible and configuration-driven so results can be rebuilt from code.
• Build monitoring for model performance, data drift and system health with alerting.
• Ship infrastructure through code review using Terraform and PR workflows.
• Document builds and raise engineering standards across the team.

Skills

Python

PyTorch

Hugging Face Transformers

timm

scikit-learn

NumPy

pandas

OpenCV

AWS SageMaker

MLflow

Hyperparameter optimisation

Reproducible training runs

Tools

Docker

Terraform

CUDA-based GPU images

SageMaker pipelines

CI/CD tooling

Job description

Senior MLOps Engineer

Product & Technology - Proximie London Office - Hybrid

Proximie is on a mission to improve healthcare by transforming the world's operating rooms into connected ecosystems of people, devices, and data.

Our Intelligence Suite transforms operating room (OR) performance, keeping teams in sync and workflows on track to maximise throughput. Simultaneously our computer vision and AI capabilities capture real-time data and detect surgical events - improving quality of data outputs. The result: ORs are optimised like never before - with predictive analytics and automated notifications ensuring patients and staff are in the right place at the right time.

Once practitioners are in the OR, our Surgical Suite enables real-time remote access and creates a secure video record of every procedure; improving training, education, and collaboration. It is an intuitive asset which helps instil a culture of continuous learning, accelerates the adoption of cutting-edge medical devices, and enhances surgical performance across the entire global workforce - improving outcomes and saving lives.

Proximie was commercialised in 2019 and is available in over 500 facilities globally.

Check out our Founder and CEO Nadine's Origins Story here: https://www.proximie.com/about-us/

Position Overview

As Proximie continues to turn every activity and event in the operating room into comprehensive, structured, and context‑rich data that drives better insights and decision making, you will be the engineer who makes that intelligence dependable at scale. Using your deep technical expertise and real‑world production experience, you will own the platform and the infrastructure that carry our machine learning solutions out of development and into hundreds of live hospitals, and keep them performing once they are there.

Our machine learning engineers own the models. You own everything that gets those models in front of a customer and keeps them running: the training and deployment infrastructure on AWS and SageMaker, reproducible and configuration‑driven training runs, environments defined in code, release pipelines that make a deployment routine rather than an event, and the monitoring that tells us when a model's behaviour shifts in a theatre on the other side of the world. You will make the difference between a solution that works and a solution a hospital can depend on.

This is a commercial team, and the work is grounded in real operational problems rather than internal roadmaps. The role demands precision, judgement and a deep understanding of how machine learning behaves in production, at scale, in environments that do not forgive downtime. If you are excited by complex problems with life‑changing impact and want to build the systems that get technology like this into the most critical environments in healthcare, we would love to hear from you.

Job Responsibilities

• Take models built by the machine learning engineers / scientists and turn them into production services that meet our latency, cost and reliability targets.
• Build and maintain SageMaker training, processing and inference workloads, and the pipelines that orchestrate them.
• Make training runs reproducible and configuration‑driven, so any result can be rebuilt from code.
• Build monitoring for model performance, data drift and system health, and make sure the right people are alerted when something moves.
• Ship infrastructure through code review rather than the console, using Terraform and a proper pull‑request workflow.
• Document what you build and raise the engineering standard around you.

Requirements

Essential

• Expert Python, with deep hands‑on PyTorch experience (TensorFlow also welcome) and the surrounding ecosystem: Hugging Face Transformers, timm, scikit‑learn, NumPy, pandas, OpenCV.
• Production experience on AWS, specifically Amazon SageMaker: training and processing jobs, pipelines, model registry, endpoints.
• Practical MLOps: experiment tracking and model versioning with MLflow, hyperparameter optimisation, and reproducible, configuration‑driven training runs.
• Containerisation and infrastructure as code: Docker and CUDA‑based GPU images, plus enough Terraform to ship your own model to an environment through code review rather than a console.
• Strong software engineering fundamentals: Git and pull‑request workflow, automated testing, CI/CD.
• Able to explain technical trade‑offs clearly to people who are not