AI Product Manager

We are looking for an experienced AI Product Manager who is passionate about building intelligent products customers love. You will join a dynamic, fast-paced environment and work with cross-functional teams — engineering, data science, design, and go-to-market — to design, build, and launch AI-powered products that deliver our company's vision and strategy. This role sits at the intersection of applied AI/ML, customer outcomes, and business strategy: you'll define what we build, why we build it, and how we measure success in a rapidly evolving AI landscape.

• →Own the AI product roadmap — including LLM-powered features, predictive models, agentic workflows, and ML-driven automation — translating customer needs into a prioritized, outcome-driven plan.
• →Develop a deep understanding of customer experience, identify product gaps where AI can deliver disproportionate value, and generate new ideas that grow market share, improve customer experience, and drive growth.
• →Partner closely with data scientists, ML engineers, and software engineers to scope models, evaluate tradeoffs between accuracy, latency, cost, and UX, and ship with quick time-to-market and optimal resources.
• →Translate product strategy into detailed requirements and prototypes, including user stories, MVP scope, acceptance criteria, model evaluation criteria, and KPI frameworks tied to measurable customer outcomes.
• →Define and monitor AI feature success metrics — including model performance (precision, recall, accuracy), user adoption, and business impact — through dashboards and regular reporting to executive leadership.
• →Conduct structured buy / build / partner analysis for new AI capabilities, including evaluation of foundation model providers, third-party AI vendors, and open-source alternatives.
• →Scope and prioritize activities based on business and customer impact, balancing near-term wins with longer-horizon AI investments.
• →Drive product launches in collaboration with marketing, PR, executives, and other PM team members — including positioning AI capabilities clearly and avoiding overpromising.
• →Evaluate promotional plans to ensure messaging accurately conveys AI capabilities and limitations, and is consistent with the broader product line strategy.
• →Act as an AI product evangelist — both internally and externally — to build awareness, understanding, and trust in our AI capabilities.
• →Represent the company by visiting customers to solicit feedback on AI features, observe real-world use, and surface opportunities for the next wave of intelligent product capabilities.

• BS/MS degree in Computer Science, Engineering, Data Science, or equivalent practical experience.
• 3–5 years of proven product management experience, with at least 1–2 years owning AI/ML-powered product features in a SaaS environment.
• Proven track record of managing all aspects of a successful product throughout its lifecycle — from discovery through launch and iteration.
• Demonstrated ability to develop product and AI strategies and effectively communicate recommendations to executive leadership.
• Solid technical background with hands-on understanding of software development, modern AI/ML technologies (LLMs, embeddings, classical ML), and SaaS architectures.
• Ability to read and interpret ML model outputs, understand evaluation metrics (precision/recall, AUROC, RMSE, F1, eval sets for LLMs), and hold technical teams accountable to model quality.
• Strong written, verbal, presentation, and communication skills — especially the ability to explain AI concepts to non-technical audiences.
• A passion for usability, user empathy, and a knack for simplicity — particularly in designing AI experiences that build user trust.
• Strong problem-solving skills and willingness to roll up your sleeves to get the job done.
• Skilled at working effectively with cross-functional teams in a matrix organization.

• Hands-on experience shipping LLM-based product features, RAG architectures, fine-tuned models, or agentic AI applications.
• Familiarity with AI governance frameworks, model explainability, evaluation harnesses, and responsible AI practices.
• Experience defining prompt engineering standards, eval pipelines, or human-in-the-loop workflows for AI products.
• Exposure to MLOps tooling and lifecycle (model training, deployment, monitoring, drift detection).

• Travel expectation of approximately 10–15% to meet with customers, partners, and team members.