Direct employment by the client – Company employee from Day 1.

A leading company is seeking an experienced ML Engineer for an end-to-end role combining Machine Learning and Cloud Infrastructure. In this position, the selected candidate will be responsible for the entire lifecycle, from initial data processing to production deployment.

Key Benefits

• Direct Employment: Company employee from Day 1.
• Hybrid Work Model: Offering flexibility and balance.
• Advanced Technology: Opportunity to work on advanced systems with real business impact.

Key Responsibilities

• End-to-End Pipelines: Design, build, and maintain end-to-end ML and ETL pipelines, including data extraction, processing, model training, validation, and deployment.
• Production & Deployment: Own the model deployment process, utilizing Docker for containerization and setting up robust production environments.
• CI/CD & GitOps: Work closely with DevOps teams, continuously managing deployment workflows using Git-based CI/CD tools.
• R&D Collaboration: Support the research and development of pricing and risk models, including supervised and unsupervised learning, segmentation, behavioral pricing, and scoring models.

Requirements

• Education: B.Sc. in Computer Science, Engineering, or a related quantitative field.
• Experience: At least 2 years of experience in a similar role managing ML systems in production environments – Must.
• Cloud Infrastructure: Significant, hands-on experience building and operating ML pipelines on AWS – Must.
• Tech Stack Mastery: Practical proficiency with Docker, Kubernetes, Git, and CI/CD processes – Must.
• Development Skills: Strong proficiency in Python with proven experience writing production-grade ML code – Must.