KEMIO Consulting
Senior Machine Learning Engineer
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Senior MLOps Engineer
AI-Enabled Drug Discovery | London
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
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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
Cloud ML infrastructure
Terraform or similar infrastructure-as-code tooling
Large-scale or foundation-model training
Experience with distributed GPU workloads, multimodal ML, biomedical AI, computational biology, genomics, imaging, or multi-omics would be particularly relevant.
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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?
Get in touch with Team @KEMIO Consulting for a confidential discussion.
#MLOps #MachineLearning #ArtificialIntelligence #DrugDiscovery #Biotech #LifeSciences #AIEngineering
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