Must Have Technical/Functional Skills
Proven track record of leading projects that bridge software engineer, data scientist , legal compliance And executive stakeholders.
Knowledge of Cloud computing knowledge (AWS, Google, Azure). Costs associated to GPU.
Management of dependencies, budgets, long term strategic value across project.
Roles & Responsibilities
Manage multiple AI initiatives from use-case identification through implementation and value realization.
Establish an AI program roadmap, milestones, dependencies, risks, and governance.
Coordinate business, AI engineering, architecture, data, security, legal, risk, and operations stakeholders.
Establish a structured process for evaluating and prioritizing AI opportunities.
Work with stakeholders to develop business cases and measurable success criteria.
Track implementation cost, benefits, business outcomes, and AI ROI.
Facilitate decisions on whether a use case should employ GenAI, traditional ML, automation, or conventional application development.
Coordinate pilots and production implementations.
Manage resource requirements, dependencies, budgets, risks, issues, and executive reporting.
Establish repeatable program-management practices for scaling AI adoption.
Drive executive-level steering committee discussions and decision-making.
Cross-Functional Collaboration: Partner with data scientists, software developers, and product managers to align technical execution with business outcomes.
Technology Evaluation: Select optimal frameworks, vector databases, cloud services (AWS, Azure, Google Cloud Platform), and hardware resources for specific workloads.
