Lead ML Operations Engineer

As the MLOps Lead , you'll spearhead the development of robust, scalable machine learning infrastructure, playing a key role in deploying and optimizing ML models for regression, classification , and clustering . Leveraging AWS services such as SageMaker, EC2, Lambda, CloudWatch, S3, RDS, Load Balancer, API Gateway , and Container Registry , along with Snowflake for advanced data management, you'll build automated ML pipelines that drive business insights. You will also handle streaming data and manage streaming ML tasks , ensuring real-time data processing and insights. SQL is essential for optimizing the management and querying of large datasets, facilitating efficient data integration and retrieval across the platform. you'll develop APIs using FastAPI and Flask , as we'll as Streamlit apps for delivering interactive analytics solutions to stakeholders. Your expertise in Docker and Kubernetes will ensure model scalability and resilience. Utilizing collaboration tools like Git , Jira , and Confluence , you'll maintain seamless workflows and drive efficient project management. Additionally, your experience with Azure AD will enhance security and identity management within our cloud infrastructure. Your role will be critical in delivering data-driven decisions that propel the company forward. More about you you're a highly skilled professional with 7 + years of experience in MLOps or DevOps, specializing in deploying models at scale, including statistical models, deep learning models , and language models . You have hands-on experience in streaming data and are adept at handling streaming ML tasks , ensuring real-time data processing and insights. SQL is one of your core strengths, enabling you to work with large datasets and optimize queries for efficient data processing in platforms like Snowflake . you're proficient with AWS services including SageMaker, Lambda, EC2, CloudWatch, S3, RDS, Load Balancer, API Gateway , and Container Registry , and have expertise in containerization ( Docker , Kubernetes ) and API development using FastAPI and Flask . Additionally, your experience with Streamlit will allow you to create engaging analytics solutions that provide actionable insights. Your experience in managing team collaboration through Git , Jira , and Confluence , along with your ability to enforce code quality with Pylint and other linters, ensures smooth workflows. You thrive in fast-paced environments, deploying Generative AI models and managing the full ML lifecycle, while your technical and problem-solving skills set you apart as an innovative leader.