AI/ML Engineer - Scientific & Research Platforms

Nicoll Curtin Technology; United Kingdom; On-site; GBP 60,000 - 80,000; Full time

Job summary

A global IT consultancy seeks an experienced AI/ML Engineer to transform research-driven ML prototypes into scalable platforms. The role requires extensive experience in pharma, biotech, or life sciences, and involves collaboration with scientists to build and optimize ML workflows. The contract is Inside IR35, requiring 3 days onsite in King’s Cross, London. The day rate is flexible depending on experience. This is an exciting opportunity to work at the intersection of ML engineering and scientific research.

Qualifications

• Real experience in pharma, biotech, life sciences, or bioinformatics.
• Strong deployment experience on AWS and Azure.
• Hands-on experience with large-scale scientific datasets.

Responsibilities

• Convert scientific experiments into ML pipelines.
• Containerise and deploy ML models.
• Work with complex scientific datasets.
• Build automated ML workflows.
• Implement MLOps best practices.
• Collaborate with domain scientists.
• Contribute to core scientific AI platforms.

Skills

Production-level Python

Modern ML tooling (e.g., Databricks, Ray)

AWS deployment

Azure deployment

Large-scale scientific datasets

CI/CD

Git

Testing

Containers

Tools

Databricks

Kubernetes

AWS SageMaker

Azure ML

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