Head of AI Labs (Agentic AI)

Job Description

Position Summary The Head of AI Labs (Agentic AI) will spearhead the organization's AI innovation agenda, overseeing the design, development, and deployment of next-generation Agentic AI solutions. This leader is responsible for establishing the AI roadmap, cultivating a high-performing technical team, validating business use cases, and accelerating the transition of AI initiatives from conceptual phases to enterprise production. The ideal candidate is a visionary AI leader with deep expertise in Large Language Models (LLMs), autonomous agents, AI orchestration frameworks, and enterprise-scale automation. They will serve as the strategic bridge between business objectives and technical execution, ensuring measurable impact and ROI from AI investments. Key Responsibilities AI Strategy \& Leadership Define and execute the enterprise-wide Agentic AI strategy, roadmap, and innovation priorities. Identify high-value AI opportunities aligned with business objectives and digital transformation goals. Establish AI governance, standards, and best practices for experimentation and deployment. Serve as the primary thought leader for Agentic AI, Generative AI, and intelligent automation. AI Labs Management Recruit, lead, and develop a multidisciplinary AI Labs team (AI Engineers, Data Scientists, Prompt Engineers, and Architects). Foster a culture of innovation, continuous learning, and rapid prototyping. Manage AI Labs budgets, resource allocation, and strategic vendor partnerships. Solution Architecture \& Development Design scalable frameworks and reference architectures for Agentic AI solutions. Lead the development of multi-agent systems, orchestration frameworks, and autonomous workflows. Oversee integration of LLMs with APIs, enterprise applications, and internal knowledge bases. Ensure AI solutions adhere to enterprise standards for security, reliability, and compliance. Innovation \& Experimentation Drive proof-of-concepts (POCs) and pilots to validate emerging AI technologies. Evaluate AI platforms, models, and tools to identify strategic advantages. Establish success criteria and measurement frameworks for all AI initiatives. Translate experimental findings into practical, scalable business solutions. Business Partnership \& Delivery Collaborate with business partners to identify and prioritize high-impact use cases. Develop comprehensive business cases and ROI assessments for AI investments. Ensure AI initiatives deliver measurable value and sustainable operational improvements. Partner with Cybersecurity, Legal, and Risk teams to ensure responsible AI adoption. Productionization \& Scaling Lead the transition of validated solutions into enterprise-grade production environments. Establish robust deployment, monitoring, and continuous improvement processes. Define operational models for AI lifecycle management and governance. Drive organizational change management and internal AI literacy efforts. Qualifications Education Bachelor's degree in Computer Science, IT, Engineering, or Data Science. Master's degree or PhD in AI, Machine Learning, or related technical discipline preferred. Experience 10\+ years of experience in software engineering, data science, or technology leadership. 5\+ years leading AI, Machine Learning, or Digital Transformation teams at scale. Proven track record of delivering AI products from conceptual stages to production. Extensive experience managing cross-functional teams and executive stakeholders. Technical Expertise Strong understanding of: LLMs and Agentic AI architectures Multi-agent systems and Prompt Engineering Retrieval-Augmented Generation (RAG) AI orchestration (LangGraph, CrewAI, AutoGen, etc.) API integration and workflow automation Cloud AI platforms (Azure, AWS, GCP) MLOps and AI governance Experience with Python, AI frameworks, and modern software development practices. Key Competencies Strategic thinking and innovation leadership AI product development and business acumen Stakeholder management and executive communication Change leadership and complex problem solving Team building, coaching, and results orientation Success Metrics Volume of validated and production-scaled AI use cases Reduction in time-to-prototype and time-to-market Targeted AI adoption and utilization rates across business units Realized business value, ROI, and operational efficiencies Measurable productivity gains attributable to AI initiatives Adherence to AI governance and security standards