Machine Learning Engineer
Core
Build and maintain infrastructure to acquire data, deploy, monitor, and upgrade core data science services for machine learning models across the enterprise.
Role type
Senior Machine Learning Engineer (MLOps/Infrastructure)
Builds
Production-grade ML deployment pipelines, APIs, and monitoring systems for enterprise data science services.
Domain
Insurance / Financial Services / Cloud Infrastructure
Deliverable
production ML models
Required skills
Python (Flask/FastAPI, OOP, unit testing), Machine Learning Engineering (Neural networks, Random forests), Cloud platforms (Azure, GCP, AWS), Infrastructure as Code (Terraform), CI/CD pipelines, Docker containerization, API operations monitoring, Model registry management, Data modeling, Cloud networking.
Preferred skills
Experience in financial services or insurance, Agile methodologies, TDD.
Technologies
Python, Flask, FastAPI, Terraform, Azure, GCP, AWS, Docker, Git
Responsibilities
Develop and maintain infrastructure for deploying ML models in real-time and batch environments; Build and maintain Python APIs to serve ML models; Design and implement CI/CD pipelines for ML model deployment; Monitor and maintain cloud-based ML services ensuring reliability and performance; Review pull requests and contribute to code quality; Support data modelling and cloud networking tasks; Contribute to the development and improvement of the model registry.
Seniority
Mid-Senior, hands-on IC
