AI Technical Product Manager

What You’ll Do

• Lead the end-to-end product lifecycle for AI/ML-driven solutions, from ideation and requirements gathering to development, launch, and iterative improvement, ensuring alignment with business objectives.
• Define and prioritize product requirements by collaborating with AI researchers, data scientists, and engineers to translate complex technical concepts into actionable roadmaps.
• Design user experiences for AI-powered features, working with UX/UI teams to create intuitive workflows and prototypes that optimize interactions with machine learning models.
• Analyze data metrics (user behavior, model performance, A/B tests) to drive product decisions, feature prioritization, and post-launch optimizations.
• Collaborate with engineering teams to integrate AI/ML models into scalable production systems, ensuring reliability, performance, and alignment with product goals.
• Evaluate emerging AI/ML technologies (e.g., generative AI, LLMs) to identify opportunities for innovation and competitive differentiation.
• Coordinate cross-functional teams (engineering, marketing, legal) to address challenges such as ethical AI deployment, bias mitigation, and regulatory compliance.
• Develop go-to-market strategies for AI products, including pricing models, customer segmentation, and sales enablement materials.
• Communicate technical concepts to non-technical stakeholders (executives, customers) to align product vision with business needs.
• Oversee AI model deployment in cloud environments, working with infrastructure teams to ensure scalability, monitoring, and cost efficiency.

Requirements

• Master’s degree or foreign equivalent degree in Computer Science, Data Science, Operations Research or closely related fields, plus 1 year of experience in AI technical product management.

• Owning end-to-end product lifecycles from inception to launch in AI/ML.
• Defining product requirements/roadmaps and collaborating with AI/ML engineering teams to design, test, and deploy models.
• UX/UI design (Figma, Sketch) for AI-driven products.
• Generative AI platforms, Machine Learning DevOps (MLOps) platforms, AI toolkits.
• Distributed Systems, Kubernetes, container orchestration, cloud computing platforms, experience with public cloud providers such as AWS, GCP, or Azure.
• Using generative AI/large language models (LLMs) and their application in products.
• Working with databases using SQL and data pipelines (python, Kafka, Spark) to test AI models and process data for AI models.
• Analyzing data/metrics to drive product decisions (e.g., model performance, user behavior).