Head of AI

We are seeking a visionary who reports to the CEO, to serve as the focal point of our internal AI strategy and lay the groundwork for an AI-enabled future across our organization. In this newly created role, you will act as the primary change agent bridging business strategy, technology infrastructure, and human capital. Your mission is to transition the company into a structured, secure, and value-driven operation - driving targeted AI adoption specifically where it yields the highest impact to boost efficiency, achieve core business goals, and foster long-term growth.
Responsibilities
Authoring, shaping, and continuously updating the company's AI Transformation Strategy for internal operations, engineering enablement, knowledge management, and workforce productivity, aligned with business goals and operational excellence.
Process Optimization & Internal Efficiency: Mapping existing workflows across various departments (Marketing, Sales, HR, Delivery, R&D, Product, etc.) to identify bottlenecks.
Deploying recurring autonomous workflows (e.g., automated reporting, monitoring) using AI agents to streamline day-to-day operational work.
Architect and manage a centralized, machine-readable repository of enterprise knowledge- leveraging AI to continuously ingest, structure, and update company data to ensure it is instantly accessible and optimized for AI context engines, RAG (Retrieval-Augmented Generation) pipelines, and employee query systems.
Democratizing Development via AI-Assisted Development: Enabling business units to leverage AI-assisted development methodologies and platforms to build custom operational workflows.
Agents Platform: Building a company-wide layer for creating, managing, and sharing AI agents that integrate directly with internal enterprise systems to support operational execution.
Governance, Security, AI Cost Management (AI FinOps)& Economics:
Measuring adoption, usage, and the true cost of AI tools and workflows across all platforms to understand their impact and optimize organizational investment.
Establishing control mechanisms for "AI cost management" and AI FinOps - making techno-economic decisions (e.g., choosing between heavy models vs. specialized open-source models).
Specialized open-source models) to optimize compute costs.
Creating a balanced AI Acceptable Use Policy with the CISO and Legal that protects organizational data while acting as a secure foundation for rapid innovation.