Job Summary:
Lead development and implementation of complex information technology projects to solve problems that may have wide impact, requiring high levels of functional integration and involving multiple disciplines to be managed. May manage a number of projects simultaneously.
The AI Technical Product Manager bridges the gap between artificial intelligence capabilities and real-world product applications, combining deep technical understanding with strategic product understanding to build AI-powered solutions that deliver measurable value.
Job-Specific Responsibilities:
Product
- Balance innovation with practical implementation, assessing technical feasibility and business impact
- Establish success metrics and KPIs for AI product initiatives
Technical Leadership
- Collaborate with data scientists, ML engineers, and software developers to translate business requirements into technical specifications
- Understand AI/ML fundamentals including model architectures, training processes, evaluation metrics, and deployment considerations
- Make informed decisions about model selection, data requirements, and infrastructure needs
- Evaluate emerging AI technologies and determine their applicability to product challenges and understand risk mitigation strategies.
Cross-Functional Collaboration
- Partner with engineering teams to prioritize features and manage the development lifecycle
- Work with design teams to create intuitive user experiences that leverage AI capabilities effectively
- Coordinate with data engineering on data pipelines, quality, and governance
- Communicate technical concepts to non-technical stakeholders including executives and customers
Product Development & Execution
- Align with Project Director on strategic priorities, customer experience and usability needs, and internal / external deadlines.
- Own the product backlog, writing detailed user stories and acceptance criteria for AI features
- Manage tradeoffs between model performance, latency, cost, and user experience
- Oversee A/B testing and experimentation frameworks to validate AI-driven improvements
- Monitor model performance in production and coordinate retraining or optimization efforts
Ethics & Risk Management
- Ensure responsible AI practices including fairness, transparency, and privacy considerations
- Identify potential biases in training data and model outputs
- Establish governance frameworks for AI model deployment and monitoring
- Navigate regulatory requirements, security needs, and compliance consideration
- Build trust and collaboration by being present on-site and engaging directly with colleagues and various constituents.
- This role is responsible for other duties as assigned.