Technical Product Manager

Job OverviewWe are seeking an experienced ML Ops Technical Product Manager to drive the strategy and roadmap for our ML Ops platform and solutions. In this role, you'll collaborate with data science, engineering, and product teams to build scalable and reliable ML infrastructure that accelerates the deployment and monitoring of ML models in production. ResponsibilitiesProduct 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.Required Skills and QualificationsBachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.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, CICD, 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.Strong analytical, organizational, and communication skills.Familiarity with Agile methodologies and project management tools (e.g., Jira,Github).Preferred QualificationsKnowledge of model interpretability and explainability tools and techniques.Experience in data privacy and compliance as it relates to ML.Prior experience with large-scale ML system deployments.

Created: 2024-11-09