AI Product Manager

Overall, Job Description
AI Product Strategy & Roadmap: Define and own the product roadmap for AI and GenAI solutions. Identify high-value AI use cases aligned with business objectives. Create business cases, benefit realization frameworks, and adoption plans. Prioritize opportunities based on value, feasibility, risk, and strategic alignment. Drive AI product vision from concept to enterprise-scale deployment
Business Management : Partner with business stakeholders to understand pain points and opportunities. Translate business requirements into product features, user stories, and AI capabilities. Conduct workshops, discovery sessions, and design-thinking exercises. Build stakeholder alignment across business, technology, risk, and operations teams.
AI Solution Design - Work closely with AI Engineers, Data Scientists, Architects, and Platform teams. Define AI workflows, user journeys, guardrails, governance requirements, and success metrics. Evaluate AI models, copilots, agents, intelligent automation, and RAG-based solutions. Ensure solutions are practical, scalable, explainable, and aligned to enterprise standards.
AI Governance & Risk - Collaborate with Risk, Compliance, Legal, Information Security, and Model Risk teams. Ensure compliance with enterprise AI governance frameworks. Define controls for responsible AI, privacy, explainability, auditability, and model monitoring. Assess AI use cases against regulatory and policy requirements.
Product Performance & Adoption - Define KPIs and outcome-based success measures. Monitor product usage, adoption, user satisfaction, and business impact. Drive continuous improvement through experimentation and feedback loops. Develop executive dashboards and performance reporting.
Vendor & Ecosystem Management - Evaluate AI vendors, platforms, and solution providers. Conduct proof-of-concepts and product assessments. Manage vendor relationships and implementation partners. Stay current with developments in AI, GenAI, agentic systems, and emerging technologies.
EDUCATION
Essential requirements: Bachelor's degree in Engineering, Computer Science, Information Technology, Data Science, or related discipline.
Preferred: MBA or equivalent business qualification preferred.
Essential requirements:
Product Management
Product strategy and roadmap development
Product lifecycle management
Agile and Scrum methodologies
Business case development
Requirements management
Stakeholder management
User experience and design thinking
AI & Technology
Generative AI and Large Language Models (LLMs)
Retrieval Augmented Generation (RAG)
AI Agents and Agentic Workflows
Machine Learning fundamentals
Prompt Engineering concepts
AI Platforms (Azure AI Foundry, AWS Bedrock, Databricks, OpenAI, Anthropic, Google Vertex AI)
API-based architectures and integrations
Data platforms and analytics ecosystems
Governance & Risk
Responsible AI principles
AI risk and control frameworks
Data privacy and security concepts
Model monitoring and observability
Regulatory and compliance awareness
Preferred:
Experience with AI copilots and enterprise knowledge assistants.
Understanding of MLOps, LLMOps, and AI Platform Engineering.
Familiarity with AI gateway technologies and model management platforms.
Experience building AI products in cloud environments.
Knowledge of banking products and processes.
Exposure to BIAN, enterprise architecture or digital transformation initiatives.

Work arrangement
No