Machine Learning Engineer

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