Job Description

Inside IR35 | Contract | 3 Days Onsite (King's Cross, London)
Day Rate: Flexible (DOE)
Pharma / Biotech / Life Sciences / Bioinformatics
To be eligible for the role, you must have a valid working visa (e.g., ILR, British citizenship, EU passport).
A global IT consultancy is seeking a highly skilled AI/ML Engineer to help transform research-driven machine-learning prototypes into scalable, production-ready platforms.
This role sits at the intersection of ML engineering, scientific computing, and cloud infrastructure, supporting advanced R&D, drug discovery, and biological data analysis.
Non-Negotiable Requirement
You must have real, professional experience in pharma, biotech, life sciences, or bioinformatics, due to the close integration with scientific research.
Role Overview
You will work alongside data scientists, computational biologists, and engineering teams to build reliable ML workflows, automate experimentation, and improve overall MLOps maturity.
This is an Inside IR35 contract requiring 3 days onsite each week in London - King's Cross.
The day rate is fully flexible depending on experience.
Key Responsibilities
Convert notebook-based scientific experiments into production-ready ML pipelines
Containerise, optimise, and deploy ML/LLM models
Work with complex scientific datasets (omics, assay, imaging, molecular, clinical, etc.)
Build automated ML workflows including training, evaluation, and monitoring
Implement MLOps best practices: CI/CD, model versioning, reproducibility, scalable orchestration
Collaborate with domain scientists to raise engineering standards
Contribute to core scientific AI platforms and internal ML tooling
Required Skills & Experience
Strong production-level Python for ML engineering
Experience with modern ML tooling (Databricks, Ray, Kubernetes, MLflow, ClearML, Weights & Biases)
Hands-on deployment experience on AWS and Azure (SageMaker, EKS, AML, AKS)
Proven experience working with large-scale scientific datasets common in pharma/biotech environments
Practical experience with LLMs, generative AI, or modern deep learning architectures
Strong engineering fundamentals: CI/CD, Git, testing, IaC, containers
Preferred (Nice to Have)
Experience with HPC or GPU-accelerated ML workloads
Familiarity with scientific libraries such as BioPython, RDKit, Scanpy
Exposure to regulatory or compliance aspects of drug discovery or clinical research
Prior experience supporting scientific teams in R&D settings
Ideal Candidate
A hybrid ML engineer who bridges scientific research and production
Strong communicator, comfortable partnering with scientific stakeholders
Proactive, organised, and effective in large-scale enterprise R&D environments

Skills Required