Senior Machine Learning Engineer

Staff MLOps Engineer – Digital Twin Healthcare Platform (AWS)

Location: London (Hybrid – 2–3 days per week in the office)

Salary: £100,000–£130,000 + equity

Right to work: Unfortunately, visa sponsorship is not available for this role

My client is a well-funded, London-based SME developing an innovative digital twin platform for healthcare. Their mission is to improve clinical outcomes by enabling more intelligent, personalised, and predictive models of patient care.

They're backed by strong funding and led by a genuinely mission-driven team. This is one of those roles where you’ll be given a tonne of autonomy and the freedom to get creative with your solutions, while still having a solid technical foundation to build from.

They're now on the lookout for a staff MLOps Engineer (ideally someone already operating at lead/staff level) who’s deep into their AWS and wants to be hands-on in shaping the future of machine learning infrastructure for real-world healthcare applications. It’s a hugely rewarding space to be in, both in terms of impact and the technical challenges.

Key responsibilities:

• Design, build, and maintain scalable MLOps infrastructure within AWS
• Develop and manage CI/CD pipelines for machine learning training and deployment
• Implement robust data pipelines to support ML workflows from ingestion through to production
• Monitor and manage performance, cost, and security across ML systems
• Collaborate with data scientists, ML researchers, clinicians, and engineers
• Drive adoption of infrastructure-as-code and automation practices

Technologies and tools:

• Machine Learning - MLOps (PyTorch/TensorFlow, HFT)
• AWS (SageMaker - Training, Pipelines, Endpoints)
• Data - AWS Glue, Lambda, Step Functions
• Airform
• Terraform, ECS
• Python (core language), Bash, (GO is advantageous but not strictly necessary)

Required experience:

• Strong hands-on experience in MLOps or ML infrastructure roles
• Deep familiarity with AWS services relevant to machine learning
• Proven experience deploying models into production (ideally using SageMaker)
• Working knowledge of infrastructure-as-code, ideally using Terraform
• Strong understanding of CI/CD, automation, and monitoring within ML environments
• Ability to work cross-functionally and communicate effectively with both technical and non-technical stakeholders

Nice to have:

• Experience in highly=regulated environments such as healthcare or finance
• Familiarity with data privacy and security standards (e.g. GDPR, HIPAA)
• Exposure to model monitoring and drift detection
• Experience managing infrastructure cost and performance optimisation on AWS

What’s on offer:

• A high-impact role in an early-stage team tackling meaningful healthcare challenges
• Significant technical ownership and input into platform design
• Competitive salary and equity package
• Hybrid working model (London-based, with 2 days per week in the office)

If you're looking to bring your MLOps expertise to a genuinely rewarding, well-backed healthcare project, please do reach out.