Junior MLOps Engineer helping to design and maintain AI/ML systems at Bupa. Collaborating with teams to operationalize machine learning models and automate workflows.
Responsibilities
Design, implement and maintain MLOps pipelines for deploying AI/ML models using Azure Machine Learning.
Build and automate CI/CD workflows for AI systems using Azure DevOps, Azure CLI, and Git-based workflows.
Collaborate with data scientists to productionize models, optimize inference performance, and streamline deployment workflows.
Implement monitoring solutions for AI systems, including performance tracking, drift indicators, logging, and alerting.
Contribute to model lifecycle management: versioning, packaging, registry updates, staging-to-production promotion and rollback.
Optimize compute, storage, and infrastructure usage for cost efficiency and reliability.
Deploy and maintain containerized applications (e.g., Flask or FastAPI services) that serve ML models.
Troubleshoot operational issues in dev/test/prod environments and support root-cause analysis.
Document architecture, workflows, and operational guidelines for broader engineering teams
Requirements
2–3 years of experience in MLOps, DevOps, Data Engineering, or related cloud engineering roles.
Hands‑on experience with Azure Machine Learning (workspaces, endpoints, model registry, compute clusters).
Solid experience building CI/CD pipelines (Azure DevOps, YAML pipelines, Azure CLI, or similar tooling).
Proficiency in Python and experience using Azure SDKs for AI/ML workflows.
Practical understanding of containerization (Docker) and orchestration concepts.
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