Senior MLOps Engineer — Production ML for Healthcare

Proximie; 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

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.