Job
Description
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: 10+ 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. Show more Show less
