Software Engineer II, ML Ops

Design, develop, and maintain cloud-based infrastructure to support the deployment and scaling of machine learning models. Implement automated pipelines for continuous integration and continuous deployment (CI/CD) of ML models, ensuring seamless transitions from development to production environments.

Collaborate closely with Data Scientists and AI teams to understand model requirements and facilitate the transition from prototype to production.

Develop APIs, microservices, and other components necessary to integrate ML models into existing systems, enabling real-time inference and decision-making.

Leverage cloud services to optimize the deployment and performance of machine learning models and associated infrastructure. Utilize services such as AWS SageMaker, Lambda, and ECS to build scalable, cost-effective solutions that support real-time ML/AI workloads.

Support AI teams by troubleshooting and resolving technical challenges related to model deployment and performance in production.

Stay up-to-date with the latest advancements in ML infrastructure, cloud computing, and AI deployment strategies. Proactively suggest and implement improvements to enhance the efficiency, reliability, and scalability of ML operations within the organization.