Senior Machine Learning Operations Engineer

This is a Fully Remote Job 1. About Our Client: The organization operates in the fintech sector, focusing on enhancing banking experiences for startups. It addresses challenges in risk decisioning by integrating machine learning models to drive real-time decisions on fraud and financial crime. To support this, the team builds and maintains a machine learning platform that streamlines the deployment and monitoring of models, ensuring fast iteration and reliable production operations. 2. About the Opportunity: The Senior Machine Learning Operations Engineer will lead the development and operation of real-time inference services that score models for risk decision engines. This role is responsible for managing the production lifecycle of machine learning models, enabling seamless deployment, observability, and retraining. The position contributes to the reliability and efficiency of the platform that powers critical business decision-making. 3. Responsibilities: Build and maintain real-time inference services with low latency and high availability Manage model deployment infrastructure, including registry, versioning, CI/CD pipelines, and rollout strategies Develop model observability tools for monitoring availability, latency, errors, and drift detection Collaborate with Risk Data Science teams for model handoff and operational management Implement experimentation features such as champion/challenger testing and explainability outputs Take ownership of platform projects and contribute to shaping the platform team's development 4. Requirements: Minimum of 5 years experience in machine learning engineering, backend software engineering, or MLOps Experience deploying and operating ML models in production with low latency and high availability Strong backend engineering skills in Python and API frameworks like FastAPI or Flask Familiarity with model lifecycle tools: registries, CI/CD, versioning, and rollout patterns Ability to build observability and alerting systems for production services, including model-specific signals Proficiency with data technologies: SQL, key-value stores (e.g., Redis, DynamoDB), and streaming pipelines (e.g., Kafka, Kinesis) 5. Pay Range and Compensation Package: US employees (any location): $166,600—$208,300 USD Canadian employees (any location): $157,400—$196,800 CAD Equal Opportunity Statement: Equal Opportunity Statement: Our client is an equal opportunity employer. They celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, or national origin. Note: TalentHop is a recruitment partner of this role. Please note that all employment decisions, including candidate assessment, interviews, hiring, compensation, and employment terms, are made exclusively by the hiring employer.