Principal AI Leader

Key Responsibilities

• AI Strategy: Translate corporate growth goals into concrete initiatives, establishing clear business and technical KPIs to measure success across all portfolios.
• Methodological Framework Design: Blueprint and execute the transition from traditional product development to an advanced Agentic Product Development Methodology across 5 core teams.
• Baseline & Data Readiness: Architect and execute data readiness initiatives to define baselines, leveraging consumer insights, analytics, and benchmarking platforms.
• Tool Audit & Integration: Audit current early-adopter tool usage, manage enterprise AI agreements, and establish standard prototyping and usability testing platform integration.
• Infrastructure Optimization: Partner with engineering leaders to optimize cloud setups and manage dedicated compute/GPU resource allocations for localized model training.
• Enablement & Change Management: Drive tool adoption by upskilling non-technical PMs and engineers, turning abstract capabilities into practical, day-to-day development workflows.

Professional Experience

• 10+ years of experience leading digital or product transformations, with at least 3+ years specifically focused on Enterprise AI strategy, LLM implementations, or Agentic workflow design.
• Technical Literacy: Strong conceptual and practical understanding of generative AI tech stacks, vector databases, model fine-tuning processes, and cloud infrastructure constraints (AWS/Azure/GCP and GPU resource management).
• Product Expertise: Proven track record in product management or technical architecture within data-dense, benchmarking, or consumer insights industries.
• Business Acumen: Demonstrated success aligning technical implementations with hard financial metrics (EBITDA, ROI, and cost-efficiency KPIs).
• Communication: Elite interpersonal and advisory skills; ability to easily communicate technical AI architectures to executive stakeholders and non-technical product teams alike.

Why Join Us

• Engage in high-impact AI modernization efforts across diverse industrial landscapes.
• Bridge functional gaps by partnering with strategy, product, data, and engineering stakeholders.
• Define the standards for ethical, responsible, and effective enterprise AI implementation.
• Acquire premier experience at the critical intersection of applied technology and business strategy.