ML OPS Engineer
Core
Design, build, automate, deploy, monitor, and support machine learning solutions across the enterprise, bridging Data Science, Data Engineering, DevOps, and Cloud Engineering teams.
Role type
Senior ML Ops Engineer
Builds
Enterprise ML Ops platforms, CI/CD pipelines, model deployment frameworks, feature management, and automated retraining processes.
Domain
Financial Services / Insurance / Cloud Native AI
Deliverable
production ML models
Required skills
Azure ML, Domino, Azure Databricks, ADF, supervised learning, unsupervised learning, model evaluation, feature engineering, model life cycle management, CI/CD pipelines, cloud-native data and AI platforms, containerisation, orchestration, Infrastructure-as-Code, model registries, batch/real-time/API inference, monitoring (performance, data quality, drift), observability, secure access controls, secrets management, Responsible AI governance.
Preferred skills
Financial Services/Insurance experience, enterprise-scale AI platforms, GenAI, LLMOps, Agentic AI frameworks.
Technologies
Azure ML, Domino, Azure Databricks, ADF, Kubernetes, Docker, Terraform, Prometheus, Grafana, MLflow.
Responsibilities
Design and implement enterprise ML Ops platforms; Build reusable ML deployment patterns and automation; Develop model serving and inference architectures; Build and maintain CI/CD pipelines for ML workloads; Convert experimental data science solutions into production-grade systems; Implement monitoring for model performance, data quality, and drift.
Seniority
Senior, hands-on IC