Senior ML Ops Engineer-Databricks
1 week ago
Job Responsibilities:- Evaluate and source appropriate cloud infrastructure solutions for machine learning needs, ensuring cost-effectiveness and scalability based on project requirements. - Automate and manage the deployment of machine learning models into production environments, ensuring version control for models and datasets using tools like Docker and Kubernetes. - Set up monitoring tools to track model performance and data drift, conduct regular maintenance, and implement updates for production models. - Work closely with data scientists, software engineers, and stakeholders to align on project goals, facilitate knowledge sharing, and communicate findings and updates to cross-functional teams. - Design, implement, and maintain scalable ML infrastructure, optimizing cloud and on-premise resources for training and inference. - Document ML processes, pipelines, and best practices while preparing reports on model performance, resource utilization, and system issues. - Provide training and support for team members on ML Ops tools and methodologies, and stay updated on industry trends and emerging technologies. - Diagnose and resolve issues related to model performance, infrastructure, and data quality, implementing solutions to enhance model robustness and reliability.Education, Technical Skills & Other Critical Requirement:- 6+ years of relevant experience in AI/ analytics product & solution delivery - Bachelor's/master's degree in an information technology/computer science/ Engineering or equivalent fields experience. - Proficiency in frameworks such as TensorFlow, PyTorch, or Scikit-learn. - Strong skills in Python and/or R; familiarity with Java, Scala, or Go is a plus. - Experience with cloud services such as AWS, Azure, or Google Cloud Platform, particularly in ML services (e.g., AWS SageMaker, Azure ML). - CI/CD tools (e.g., Jenkins, GitLab CI), containerization (e.g., Docker), and orchestration (e.g., Kubernetes). - Experience with databases (SQL and NoSQL), data pipelines, ETL processes, ML pipeline orchestration (Airflow) - Familiarity with monitoring and logging tools such as Prometheus, Grafana, or ELK stack. - Proficient in using Git for version control. - Strong analytical and troubleshooting abilities to diagnose and resolve issues effectively. - Good communication skills for working with cross-functional teams and conveying technical concepts to non-technical stakeholders. - Ability to manage multiple projects and prioritize tasks in a fast-paced environment.
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