AI Transformation Lead

Valsoft Corporation

11h

About The Position

The AI Transformation Lead is responsible for identifying, building, validating, and scaling AI-powered product solutions that drive real customer and business outcomes. This role is hands-on and AI-first. An AI Lead does not operate through documents, tickets, or handoffs. They build working solutions using AI, engage directly with customers, and iterate rapidly based on real-world feedback. The AI Transformation Lead does everything an AI Product Engineer does - plus owns customer discovery, validation, prioritization, and measurable impact.

Requirements

Strong ability to build working software using AI tools
Proven experience rapidly prototyping and iterating on product ideas
Deep understanding of probabilistic AI systems and guardrails
Ability to reason about systems end-to-end
Experience engaging directly with customers
Ability to identify business and revenue opportunities
Strong product judgment and taste
Comfort prioritizing under uncertainty
Ability to explain complex AI behavior clearly and honestly
Strong storytelling grounded in real system behavior
Comfortable leading through influence rather than authority

Responsibilities

AI-First Product Building
Rapidly prototype, iterate, and refine solutions to validate ideas early
Use AI agents for requirements analysis, design exploration, implementation, and testing
Move from problem understanding to working solution without reliance on legacy SDLC rituals
Customer Discovery & Engagement
Engage directly with customers to understand workflows, pain points, and unmet needs
Use live demos and working prototypes to drive customer conversations
Validate solutions through real usage, not assumptions or opinions
Build trust by clearly explaining AI behavior, limitations, and tradeoffs
Problem Framing & Opportunity Identification
Frame problems clearly and precisely before execution
Identify new AI-powered solution opportunities and revenue potential
Evaluate ideas based on impact, feasibility, and learning velocity
Use experimentation to discard weak ideas quickly
Outcome Ownership
Define success in terms of adoption, retention, efficiency, and revenue impact
Prioritize initiatives based on outcomes, not feature volume
Continuously assess whether solutions are creating real value
Own the full lifecycle from idea to impact
Cross-Functional Leadership
Work closely with AI Product Engineers and AI Developers within AI Pods
Align engineering, design, and data efforts around outcomes
Communicate clearly with internal stakeholders
Represent the product externally with credibility and clarity