Head of Enterprise AI Solutions

Location:

US-NJ-Bridgewater, US

The Head of Enterprise AI Solutions is a leader responsible for owning the Enterprise AI product portfolio end to end, including AI product strategy, delivery, governance, and responsible deployment at scale.

This is a hands‑on leadership role combining technical depth, product ownership, and people leadership. The Head of Enterprise AI Solutions builds and leads a team of AI engineers and product-oriented practitioners while remaining actively involved in designing, building, and shipping AI products, particularly AI agents that reason, use tools, and execute end-to-end business workflows.

Success in this role is measured by AI solutions or products delivered and adopted, business outcomes achieved, and the establishment of a trusted, scalable AI governance framework that enables innovation while managing risk.

• Own the enterprise AI solutions strategy and roadmap, aligning AI investments to strategic business priorities.
• Reimagine enterprise workflows with an AI first product mindset, identifying and prioritizing high value opportunities for AI driven transformation.
• Partner with executive and business leaders to frame ambiguous problems, quantify value, and translate opportunities into clear AI solution hypotheses.
• Define and own product KPIs for each AI product; track adoption, usage, and business impact post-launch, reporting outcomes to executive stakeholders.
• Drive product/solution discovery and validation through user research, prototyping, and structured experimentation before committing to full-scale build.
• Embed MVP discipline into AI solution delivery scope minimum viable releases, gather user feedback early, and iterate based on observed outcomes.
• Serve as the voice of the internal customer, maintaining continuous feedback loops with business users to inform backlog priorities and solution direction.
• Provide dotted-line leadership to AI Agent Builders embedded within business functions, ensuring alignment to enterprise AI standards, architecture, governance, and delivery practices.
• Define and maintain the operating model for how function-embedded Agent Builders collaborate with the central AI Solutions team including shared tooling, code standards, model selection guidelines, reusable components, and escalation paths.
• Partner with functional leaders to scope Agent Builder roles, support hiring and onboarding, and ensure embedded talent is equipped to build and deploy AI agents that meet enterprise-grade quality and compliance requirements.
• Own and lead AI governance across the enterprise, including policies, standards, guardrails, and operating models.
• Establish and enforce Responsible AI practices, including model risk management, human-in-the-loop design, escalation paths, monitoring, and auditability, with specific attention to regulatory requirements in the pharmaceutical and Med Tech space (e.g., FDA, HIPAA, GxP, and applicable data privacy regulations).
• Remain actively hands on in designing and building AI products and agent based solutions.
• Build and review AI agents using Python, LLM APIs, and modern agent frameworks that analyze information, call tools/APIs, and complete tasks end to end.
• Ensure production ready delivery using enterprise AI platforms (e.g., Azure AI services), with strong security, observability, and reliability.
• Build, lead, and develop a high performing AI Solutions team.
• Foster a culture of product ownership, build first execution, and accountability.
• Operate within Agile product delivery models, managing backlogs, iterative releases, and outcome-based prioritization.
• Balance speed of innovation with enterprise grade quality, governance, and operational stability.
• Lead AI product or solution delivery within Agile frameworks, including ownership of product backlogs, sprint planning, backlog refinement, release planning, and sprint retrospectives.
• Accountable for iterative, outcome-based delivery — managing scope, schedule, and quality across concurrent AI product work streams.
• Track team-level delivery metrics (velocity, cycle time, release cadence) and drive continuous improvement in execution.

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