Overview
A regulated analytics organisation is moving machine learning from isolated experiments into a shared production platform. The challenge is not simply model deployment. The programme needs a coherent approach to lineage, reproducibility, access control and monitoring so that models can be operated and reviewed long after the original development team has moved on.
Responsibilities
- Define the target architecture for model training, deployment and monitoring.
- Establish standards for model lineage, reproducibility and controlled promotion.
- Work with data and platform teams on secure infrastructure patterns.
- Review existing machine learning workflows and identify production risks.
- Guide engineering teams through adoption of common MLOps practices.
Requirements
- 8+ years of software, data or machine learning engineering experience.
- Strong production MLOps background rather than research only experience.
- Deep Python and cloud platform knowledge.
- Experience with MLflow, Kubernetes and automated model deployment.
- Understanding of governance, auditability and access controls for regulated environments.
Expertise
Cloud Architecture
Kubernetes
MLflow
MLOps
Model Governance
Python