* 5+ years of experience in product management, preferably with a focus on ML Ops, data science, or machine learning infrastructure. * Strong understanding of ML Ops tools and platforms, including ML pipelines, CI/CD, model versioning, and monitoring frameworks. * Technical expertise in machine learning, data engineering, and DevOps methodologies * Experience with cloud platforms (AWS, Azure, Google Cloud) and their ML services, data platforms like BigQuery. * Familiarity with Agile methodologies and project management tools (e.g., Jira, Github). * Experience developing with containers and Kubernetes in cloud computing environments * Exposure to write, run SQL queries to validate data availability & quality * Experience of reading through code repos to understand logic * Experience in managing, tracking progress across key MLOps stages: data sourcing, feature engineering, model training including hyperparameter tuning if any, testing and deployment. * Exposure to Vertex AI, MLFlow, Kubeflow * Exposure to implementing model governance frameworks & reproducibility standards. * Exposure to setting up/interpreting dashboards for model performance. Preferred - * Knowledge of model interpretability and explain ability tools and techniques. * Experience in data privacy and compliance as it relates to ML. * Prior experience with large-scale ML system deployments. Roles & Responsibilities: * Product Strategy & Roadmap: Define and prioritize the ML Ops product roadmap by assessing business goals, customer needs, and emerging industry trends in ML Ops. * Cross-functional Collaboration: Work closely with data scientists, ML engineers, DevOps, and software engineers to ensure seamless integration and deployment of ML models. * Project Management: Coordinate and manage timelines, resources, and deliverables across multiple teams to keep projects on track. * Model Lifecycle Management: Oversee the end-to-end ML model lifecycle, including data preparation, model development, deployment, monitoring, and maintenance. * Automation & Scaling: Identify opportunities for automation and scalability in the ML pipeline, from data ingestion to model deployment. * Monitoring & Optimization: Develop and implement monitoring and alerting frameworks for model performance and data quality. Partner with engineering teams to troubleshoot and optimize pipelines. * Stakeholder Communication: Serve as the primary point of contact for internal and external stakeholders. Communicate product updates, metrics, and results to senior leadership. * Risk Management: Identify, assess, and mitigate risks related to ML model deployment, including ethical considerations, data privacy, and regulatory compliance. * Documentation & Training: Develop clear and comprehensive documentation for ML Ops processes and workflows. Provide training to teams on best practices. #LI-RJ2 Salary Range - $100,000-$150,000 a year