AI Program Lead

Key Responsibilities

• Audit workflows: Identify departments and processes that will benefit most from AI.
• Vendor management: Evaluate, select, and implement tools (e.g., OpenAI API, Microsoft Copilot, Claude).
• Data oversight: Partner with IT to ensure data security and compliance with PII standards.
• Training & enablement: Create prompt engineering guidelines and develop training materials for end users.
• Foundation building: Establish internal policies, guardrails, and adoption mechanisms to scale AI safely.
• Roadmap & stakeholder alignment: Develop an AI roadmap (scope, prioritization, coordination) with internal stakeholders.
• Prototyping: Prototype and demonstrate small solutions using AI platforms (e.g., Copilot) to validate value.
• Talent planning: Identify future hiring needs and skills required to support the AI roadmap.

Core Competencies

• Programming: Proficiency in Python and familiarity with frameworks such as PyTorch or TensorFlow.
• LLMOps: Ability to operationalize large language models efficiently, including monitoring, reliability, and cost control.
• Data governance & security: Working knowledge of PII, data access controls, and secure AI practices.
• Communication & influence: Ability to explain AI concepts and decisions to non-technical stakeholders and drive change across teams.

Success Measures (What Good Looks Like)

• Defined AI use cases per function (e.g., HR, Finance, Operations, Business Development).
• Measurable productivity KPIs tied to prioritized workflows.
• Structured pilot-to-scale approach with governance and adoption support.
• 3–5 high-impact workflows identified and validated for value and feasibility.
• Data exposure risks assessed and mitigated in partnership with IT & Security.
• Training program launched with internal champions to drive responsible usage.