Senior Machine Learning Engineer

KEMIO Consulting; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

KEMIO Consulting is seeking a Senior MLOps Engineer to advance production ML models from research to scalable deployment, at the intersection of AI engineering and biomedical R&D.

You will own deployment, monitoring, retraining, and lifecycle management of models, while enabling reproducible and trustworthy AI in drug discovery contexts.

Qualifications

• Hands-on production ML systems experience.
• Experience with distributed GPU workloads and biomedical AI.
• Familiarity with ML tooling and MLOps standards.

Responsibilities

• Own production ML models across deployment, monitoring and lifecycle management.
• Operate and troubleshoot distributed training and fine-tuning workloads.
• Manage experiment tracking, model registries and full model provenance.
• Deploy and optimise model-serving endpoints.
• Support structured handover from ML engineering into production.
• Drive strong MLOps standards across a highly technical AI environment.

Skills

Production ML systems

Distributed training

Experiment tracking

Model registries

Model provenance

CI/CD for ML

Infrastructure as code

Biomedical AI

Genomics

Imaging

Tools

PyTorch Distributed

DeepSpeed

FSDP

Ray Train

MLflow

Weights & Biases

CI/CD for ML workflows

Terraform

Job description

A leading global life sciences organisation is expanding its AI capabilities and is looking for a Senior MLOps Engineer to help move advanced machine learning models from research into reliable, scalable production.

This is not a conventional platform engineering role.

You will sit at the intersection of AI engineering, large-scale machine learning and biomedical R&D, supporting models that may influence decisions around therapeutic targets, disease indications and patient populations.

What you’ll be responsible for :

• Owning production ML models across deployment, monitoring, retraining and lifecycle management
• Operating and troubleshooting distributed training and fine-tuning workloads
• Managing experiment tracking, model registries and full model provenance
• Deploying and optimising model-serving endpoints
• Supporting structured handover from ML engineering into production
• Driving strong MLOps standards across a highly technical AI environment

What we’re looking for

Strong hands-on experience with production ML systems, ideally including:

• PyTorch Distributed, DeepSpeed, FSDP or Ray Train
• MLflow, Weights & Biases or equivalent
• CI/CD for ML workflows
• Terraform or similar infrastructure-as-code tooling
• Experience with distributed GPU workloads, multimodal ML, biomedical AI, computational biology, genomics, imaging or multi-omics would be particularly relevant.

Life sciences experience is valuable, but not essential. What matters most is the ability to operate technically demanding ML systems reliably at scale.

Why this role?

The challenge is not simply getting models into production.

It is ensuring advanced scientific AI remains reproducible, trustworthy, performant and useful once it gets there.

Interested in working at the intersection of production AI and real-world drug discovery?