AI Product Manager

Reference: REC000625-MNS-1

We are looking for an AI Product Manager to own end-to-end AI product strategy, guide model choices, and scale high-value AI solutions across complex enterprise environments! Duties & Responsibilities In this role, you will define, deliver, and optimize AI products across platforms and diverse use cases. You will turn raw business opportunities into clear product roadmaps, balance complex technical trade-offs alongside ML engineers, and ensure every model delivers measurable, responsible ROI.
The Tech Stack Deep-Dive
Core Technologies & Frameworks

• AI / ML / GenAI Frameworks: Foundational knowledge in Generative AI, LLMs, and Machine Learning models.
• Cloud & Monitoring: Azure Monitor, Application Insights, or equivalent cloud ecosystem tools.
• Analytics & Business Intelligence: Power BI, Tableau, Qlik, or similar metrics tracking platforms.

Must-Haves

• Solid experience in Agile product management, backlog prioritization, user story development, and SDLC practices.
• Hands-on experience with AI/data products, including data readiness, governance, and model evaluation (PoCs, A/B testing).
• Strong understanding of Responsible AI, privacy, ethical AI, and regulatory compliance frameworks.
• Proven business acumen: ability to build business cases, cost-benefit analyses, financial models, and pricing strategies (subscription, consumption, or outcome-based).
• AI/Cloud Certifications (e.g., Microsoft AI, Claude Associate, OpenAI, AWS, Google, or equivalent).

Nice-to-Haves

• Bachelor's degree in CS, Data Science, Information Systems, or Engineering.
• Agile/Product Certifications (e.g., PSPO, CSPO, SAFe POPM, CBAP).
• Background in large-scale digital transformation or enterprise tech environments.

Key Responsibilities (Keep it brief)

• Define Strategy & Requirements: Translate complex business needs into detailed Product Requirement Documents (PRDs), defining target baselines, fallback needs, and human-in-the-loop workflows.
• Guide Model & Tech Choices: Partner with Data Scientists and ML Engineers to evaluate model trade-offs involving quality, cost, latency, explainability, and security.
• Establish Evaluation & Telemetry: Build metric frameworks, baselines, and telemetry to track model accuracy, performance, cost per successful outcome, and adoption over time.
• Drive Responsible AI & Release: Lead governance, privacy, and compliance readiness while translating technical concepts clearly to executive stakeholders.

Why Join Us?

• High Impact & Ownership: Own the full AI product lifecycle—from zero-to-one product discovery to operational scaling—with direct influence over architecture, pricing, and roadmap strategy.
• Collaborative Tech Culture: Work shoulder-to-shoulder with top-tier ML Engineers, Data Scientists, and Research Teams using modern AI capabilities.
• Enterprise Innovation: Drive tangible business outcomes and shape responsible AI practices at scale