Head of Artificial Intelligence

Position Summary

We are seeking a senior, hands-on AI engineering leader to build and scale enterprise AI capabilities across a large, complex organization. Reporting to executive leadership, this role owns the strategy, architecture, development, deployment, and continuous improvement of reusable AI services and platforms spanning AI/ML, Generative AI, Agentic AI, optimization, digital twins, IoT, and conversational analytics.

The successful candidate will lead a multidisciplinary organization of AI engineers, data scientists, ML engineers, and applied AI specialists, while remaining deeply involved in technical decision-making. The role requires extensive experience across multiple generations of AI technologies, from predictive analytics and machine learning through deep learning, large language models, agentic systems, and AI-driven automation.

This leader will establish scalable AI products, engineering standards, evaluation frameworks, operational disciplines, and governance practices that enable AI adoption across multiple business domains. The ideal candidate combines strong technical depth with product thinking, organizational leadership, and a proven track record of delivering measurable business outcomes through enterprise-scale AI deployment.

Key Responsibilities

1. Define AI Capabilities & Services Strategy and Technical Architecture

• Develop and execute the AI capabilities roadmap aligned to enterprise objectives.

• Establish reusable AI services, frameworks, reference architectures, and engineering standards.

• Drive technical decision-making across AI platforms, models, agents, and infrastructure.

2. Build and Lead AI Engineering Powerhouse

• Lead and mentor teams of AI engineers, ML engineers, data scientists, and AI product specialists.

• Develop organizational capabilities across machine learning, GenAI, agentic AI, optimization, and digital twin technologies.

• Foster a culture of technical excellence, innovation, accountability, and continuous learning.

3. Architect and Scale Agentic AI and GenAI Systems

• Design enterprise-grade AI agent architectures incorporating orchestration, retrieval, tool use, memory, and human oversight.

• Define standards for prompt engineering, retrieval-augmented generation (RAG), evaluation, safety, observability, and governance.

• Ensure solutions are scalable, reliable, secure, and cost-effective.

4. Drive Hands-On AI Engineering Excellence

• Review model architectures, data pipelines, feature engineering, training methodologies, and agent performance.

• Partner closely with engineering teams to improve accuracy, reliability, latency, cost efficiency, and user outcomes.

• Lead root-cause analysis and continuous optimization of deployed AI systems.

5. Deliver Production-Grade AI Solutions

• Translate business opportunities into scalable AI products and services.

• Embed AI capabilities into operational workflows, customer experiences, and decision-making processes.

• Ensure solutions meet enterprise requirements for security, resilience, maintainability, and operational support.

6. Establish AI Operations and Reliability Practices

• Implement AI/MLOps, LLMOps, and AgentOps disciplines across the full lifecycle.

• Define standards for experimentation, deployment, monitoring, evaluation, incident management, and continuous improvement.

• Drive AI platform reliability, governance, and operational excellence.

7. Manage AI Portfolio and Investment Priorities

• Identify and evaluate new AI opportunities and emerging technologies.

• Prioritize investments based on business value, technical feasibility, scalability, and ROI.

• Make strategic trade-offs regarding build-versus-buy decisions, technology selection, and resource allocation.

8. Lead Cross-Functional AI Execution

• Collaborate with technology, data, security, governance, and business stakeholders to accelerate delivery.

• Resolve dependencies, align priorities, and drive execution across complex enterprise environments.

• Communicate technical trade-offs and recommendations to senior leadership.

9. Champion Responsible AI and Engineering Governance

• Ensure compliance with responsible AI principles, privacy requirements, security standards, and regulatory obligations.

• Establish governance frameworks, risk controls, and deployment readiness criteria.

• Promote sustainable engineering practices that balance innovation speed with long-term platform health and maintainability.