Vice President of Artificial Intelligence

Position Overview

As the VP of AI, you will be responsible for defining and executing the company’s AI strategy across products, operations, and infrastructure. You will lead cross-functional teams of data scientists, machine learning engineers, and AI researchers, while partnering closely with product, engineering, and executive leadership to embed AI capabilities that drive measurable impact.

Key Responsibilities

• Strategic Leadership: Develop and drive the overall AI roadmap aligned with business goals and product strategy.
• AI System Development: Oversee the design, development, and deployment of AI/ML solutions, including NLP, computer vision, predictive modeling, and generative AI applications.
• Team Leadership: Build, mentor, and scale a world-class team of AI professionals (ML engineers, data scientists, AI researchers).
• Cross-Functional Collaboration: Work closely with product, engineering, and business stakeholders to integrate AI into core offerings and operations.
• Innovation & Research: Stay ahead of trends in AI (e.g., LLMs, reinforcement learning, foundation models), identifying areas where new approaches can be applied to solve complex business problems.
• Governance & Ethics: Implement responsible AI practices, ensuring model explainability, fairness, and compliance with ethical and legal standards.
• Infrastructure & Scalability: Partner with data and cloud engineering teams to build scalable and reliable ML pipelines and AI platforms.
• Measurement & Impact: Define KPIs and success metrics for AI initiatives; ensure projects drive clear ROI and business outcomes.

Qualifications

Must-Haves:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field (PhD preferred).
• 10+ years of experience in data science, AI, or ML roles, including 5+ years in leadership or executive roles.
• Deep expertise in machine learning, deep learning, and AI technologies (e.g., TensorFlow, PyTorch, OpenAI, Hugging Face).
• Experience with cloud platforms (e.g., Azure, AWS, or GCP) and MLOps practices.
• Proven track record of successfully building and scaling AI teams and systems in production environments.
• Strong knowledge of data strategy, model lifecycle management, and AI governance.
• Excellent communication and executive presentation skills.

Nice to Have:

• Experience in a regulated industry (e.g., healthcare, finance, insurance).
• Familiarity with LLMs, generative AI, or multimodal AI applications.
• Publications, patents, or recognized thought leadership in AI/ML.