Role Overview
We are seeking an AI Delivery Lead to own the path from business problem to shipped, adopted AI product. This role is the single point of accountability between business stakeholders and the engineering team responsible for shaping what gets built, defining what "done" means, and ensuring delivered solutions produce measurable value.
The candidate will be technically credible enough to design solution architecture and challenge engineering decisions, and commercially fluent enough to negotiate scope with executives. This is a leadership and delivery role, though depth in AI system design is essential.
Key Responsibilities
Demand Shaping and Prioritization
- 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.
Solution Architecture
- Define target architecture for AI products 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.
Delivery Ownership
- Own the full delivery lifecycle: 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 alone.
- Manage delivery risk actively, escalate early, and keep commitments realistic against engineering capacity.
Stakeholder and Governance Management
- Serve as the primary interface for business sponsors running discovery sessions, demos, steering reviews, and executive status reporting.
- Calibrate stakeholder expectations on what current AI can and cannot reliably do, and manage the gap between demo and production.
- Shepherd solutions through security, legal, privacy, and responsible AI review, and maintain documentation of model use, data handling, and approved use cases.
Value Realization
- Define and track benefit metrics adoption, time saved, quality improvement, cost avoided 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.
Preferred Qualifications
- Prior hands-on engineering or data background.
- Experience with Azure AI Foundry, AWS Bedrock, Google Vertex AI, or comparable enterprise AI platforms.
- Familiarity with AI governance frameworks.
- Experience managing vendor relationships and negotiating commercial terms.
- Product management experience or formal certification in Agile, PMP, or an architecture framework such as TOGAF.
- Experience building an AI delivery function from an early or ad hoc state.
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