MLOps Engineer

A leading financial organization is seeking an experienced MLOps Engineer to join a growing data and AI department responsible for delivering advanced machine learning solutions in production environments.

This is a hands-on role focused on designing, building, and operating scalable ML infrastructure, enabling the deployment of sophisticated risk, pricing and analytics models that support critical business decisions.

Key Responsibilities

• Design, build, and maintain end-to-end ML and ETL pipelines, from data ingestion and processing through model training, validation, deployment, and monitoring.

• Own the production lifecycle of machine learning models, including containerization, deployment, scaling, and operational support.

• Build and manage cloud-native ML infrastructure on AWS.

• Collaborate closely with Data Scientists, Risk Analysts, Data Engineers, and DevOps teams to streamline model delivery and operational excellence.

• Implement and maintain Git-based CI/CD and GitOps workflows for ML applications and services.

• Support the development and deployment of supervised and unsupervised machine learning models, including risk assessment, segmentation, behavioral pricing, and scoring models.

• Drive automation, reliability, scalability, and best practices across the ML platform.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related technical field.

• 2+ years of experience in MLOps, Machine Learning Engineering or similar roles supporting production ML environments.

• Proven experience designing and operating machine learning pipelines on AWS.

• Strong hands-on expertise with Python and production-grade ML code.

• Deep understanding of Docker, Kubernetes, Git, CI/CD, and cloud-native deployment methodologies.

• Experience building and maintaining scalable ML infrastructure and automated deployment processes.

• Strong problem-solving skills and ability to work in a highly collaborative environment.

Why Join Us?

• Work on mission-critical AI and machine learning initiatives within a leading financial organization.

• Influence the architecture and evolution of enterprise-scale ML platforms.

• Collaborate with top-tier data, analytics and engineering professionals.

• Build production systems that directly impact business strategy, risk management and customer experience.