Machine Learning Engineer

CompanyInsight Global

LocationLondon Area, United Kingdom

Posted At1/19/2026

UK Visa Sponsorship Analytics

Occupation Type

Programmers and software development professionals

Occupation Code Skill LevelHigher Skilled

Sponsorship Salary Threshold

£54,700 (£28.05 per hour)

Occupation rate applies

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Description

Insight Global is looking for a seasoned ML Ops Engineer to join one of our largest pharmaceutical manufacturing clients who are building a new ML Ops function to unify their newly merged Research and Development landscape. Previously, the two groups used different tools, processes, cloud environments, and workflows. This role will focus on consolidating multiple ML and data stacks into one standardised platform. The ideal candidate will collaborate with global teams to bring together AWS‑based ML systems/infrastructure and Azure Databricks–based ML systems and pipelines, enabling shared insights and consistent ML delivery. The role involves platform engineering, Infrastructure‑as‑Code (IaC), Kubernetes‑based deployment, and establishing a reliable ML lifecycle on top of bare‑bones AWS and Azure environments.

Responsibilities Include:

Consolidate up to five different ML/data stacks into a single unified ML Ops platform for R&D projects
Bring together AWS‑focused environments and Azure Databricks pipelines into one standardised approach
Contribute to defining the future state of the platform, such as Databricks on AWS or Azure Databricks

Build and maintain IaC to standardise deployment, configuration, and cloud resource provisioning

Support and operate ML model deployment and orchestration using Kubernetes, with pipelines operating across hybrid clouds

Enhance and scale the current AWS foundation with ML lifecycle tooling and operational processes

Implement unified ML pipelines, CI/CD workflows, and reproducible environments

Work closely with the team lead, fellow ML engineers, IT Ops, Data Engineering, and R&D scientists

Implement ML systems on top of consolidated infrastructure to ensure smooth end‑to‑end delivery

Must Haves

Senior ML Ops engineering experience including version control, infrastructure, tooling, automation, model governance, and deployment
Proven experience working across hybrid cloud environments (AWS and Azure)
Databricks experience on AWS and/or Azure
Strong Infrastructure‑as‑Code (IaC) skills using Terraform to build, deploy, and manage cloud infrastructure for ML platforms
Experience deploying ML models using Kubernetes
Familiarity with combining multiple cloud services into a coherent ML platform

Strong understanding of ML CI/CD pipelines, including how to automate model building, testing, deployment, and monitoring

Plusses

Experience consolidating or migrating ML or data platforms
Background working in environments undergoing cloud and/or tool unification
Experience with ML lifecycle tooling such as MLflow or feature stores
Previous experience working for enterprise pharmaceutical companies
PhD or master’s degree in a relevant field