Lead Engineer AI

Location: Northbrook, Illinois

Job Summary

The Lead AI Engineer is a hands-on AI, data, analytics, and automation practitioner embedded within a business domain such as Commercial, Operations, Product, or Corporate Functions. The role identifies, designs, prototypes, and delivers practical solutions that drive measurable business value.

• The Lead AI Engineer works directly with business teams to understand workflows, decisions, pain points, and opportunities, then rapidly translates them into working proofs of concept, pilots, demonstrations, automations, agents, copilots, analytics solutions, and decision-support tools.

This is a hands-on builder role. The Senior AI Engineer is expected to use modern AI-assisted development tools and approved enterprise platforms to create solutions directly, not simply define requirements or coordinate delivery. The role owns outcomes, not tickets.

• The Lead AI Engineer selects the simplest effective approach. A solution may use process improvement, traditional workflow automation or RPA, analytics, machine learning, generative AI, or a combination of these approaches. The role determines what can be delivered directly and what should transition to engineering, data science, platform, product, or automation teams for production deployment and scale.
• The role serves as the bridge between business stakeholders and technical teams, combining strong communication, business understanding, product thinking, technical problem solving, and hands-on execution. The Lead AI Engineer remains engaged through testing, transition, adoption, and measurable value realization.

Trusted Partnership & Opportunity Identification

• Embed within a business domain and develop a deep understanding of its processes, workflows, systems, decisions, handoffs, and operational challenges.
• Build trusted relationships with business leaders and frontline users; serve as a primary partner for AI-, data-, analytics-, and automation-enabled problem solving.
• Continuously identify opportunities to improve decisions, productivity, customer experience, quality, cycle time, or business performance.
• Shape and manage demand, focusing teams on opportunities with the highest potential value, feasibility, and readiness.
• Challenge existing ways of working and help teams envision materially different workflows, not only incremental improvements.