ML Ops Engineer

ML Ops Engineer to drive the full lifecycle of machine learning solutions-from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering.

Key Responsibilities

• Develop predictive models using structured / unstructured data across 10+ business lines, driving fraud reduction, operational efficiency, and customer insights.
• Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code / no-code model development, documentation automation, and rapid deployment
• Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
• Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).
• Implement CI / CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
• Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
• Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs

Qualifications