AI Delivery Lead

Diversified Systems is searching for an AI Delivery Lead to own the path from business problem to shipped, adopted AI product. This role serves as the single point of accountability between business stakeholders and the engineering team, responsible for shaping what gets built, defining what success looks like, and ensuring delivered solutions produce measurable business value.

About the role

The candidate must be technically credible enough to design solution architecture and challenge engineering decisions, while also possessing the commercial and leadership skills necessary to negotiate scope, manage stakeholder expectations, and drive delivery outcomes. This is a leadership and delivery role with strong emphasis on AI system design, governance, and value realization.

Responsibilities

• Run intake for AI use-case requests, assess each for business value, feasibility, data readiness, and risk, and maintain a prioritized delivery roadmap.
• Translate ambiguous business problems into scoped requirements, success criteria, and technical designs the engineering team can execute against.
• Define target architecture for AI products, including agent design, retrieval strategy, model selection, integration points, and data flows, in partnership with senior engineers.
• Set and enforce architectural standards, reusable patterns, and build-versus-buy decisions across the AI portfolio.
• Evaluate models, platforms, and vendors, and own the technical case behind each selection.
• Own the full delivery lifecycle including scoping, estimation, sprint planning, dependency management, risk mitigation, release, and hypercare.
• Define acceptance criteria appropriate to probabilistic systems, including evaluation sets, accuracy and quality thresholds, latency budgets, and fallback behavior, recognizing that AI features cannot be accepted on binary pass/fail criteria alone.
• Manage delivery risk actively, escalate early, and keep commitments realistic against engineering capacity.
• Serve as the primary interface for business sponsors, running discovery sessions, demonstrations, steering reviews, and executive status reporting.
• Calibrate stakeholder expectations on what current AI can and cannot reliably do and manage the gap between demonstrations and production implementations.
• Shepherd solutions through security, legal, privacy, and responsible AI reviews, and maintain documentation of model use, data handling, and approved use cases.
• Define and track benefit metrics including adoption, time saved, quality improvement, and cost avoidance, and report outcomes against the original business case.
• Own AI platform and inference cost management, including budget forecasting and per-workload cost attribution.
• Partner with enablement and change management teams to drive adoption after launch.

Requirements

• Experience translating business problems into AI solution requirements required.
• Experience leading AI product delivery from concept through implementation required.
• Experience with solution architecture, including AI agent design, retrieval strategies, model selection, integrations, and data flows required.
• Experience defining architectural standards, reusable patterns, and build-versus-buy decisions required.
• Experience evaluating AI models, platforms, and vendors required.
• Experience managing full delivery lifecycles, including sprint planning, dependency management, risk mitigation, release management, and hypercare required.
• Experience defining acceptance criteria for AI solutions, including evaluation datasets, quality metrics, latency requirements, and fallback strategies required.
• Experience facilitating executive stakeholder engagement and business sponsor communications required.
• Experience supporting governance, security, privacy, and responsible AI review processes required.
• Experience establishing and tracking AI adoption, business value, and operational metrics required.
• Experience managing AI platform costs, forecasting budgets, and monitoring inference costs required.
• Strong communication, leadership, negotiation, and stakeholder management skills required.

Preferred Qualifications

• Prior hands-on engineering or data background highly desired.
• Experience with Azure AI Foundry, AWS Bedrock, Google Vertex AI, or comparable enterprise AI platforms highly desired.
• Familiarity with AI governance frameworks highly desired.
• Experience managing vendor relationships and negotiating commercial terms highly desired.
• Product management experience highly desired.
• PMP certification highly desired.
• Agile certification highly desired.
• TOGAF or comparable architecture certification highly desired.
• Experience building an AI delivery function from an early or ad hoc state highly desired.

About Diversified Systems

Founded in 1990, Diversified Systems is an award-winning Technology Services corporation providing all levels of IT project consulting services nationwide. DSI is headquartered in Columbus, Ohio with regional offices in the American Midwest and East Coast.