AI Technical Product Manager

Supio•Seattle, WA

29d•$101,317 - $180,000

About The Position

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.
Must have 1 year of experience/skills in the following:
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).

Responsibilities

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.