AI Delivery Lead Architect

Role Overview

We are seeking an AI Delivery Lead Architect to own the path from business problems to shipped, adopted AI products. This role serves as the single point of accountability between business stakeholders and the engineering team. The AI Delivery Lead is responsible for shaping what gets built, defining what ‘done’ means, and ensuring delivered solutions produce measurable value.

The ideal candidate is technically credible enough to help design solution architecture and challenge engineering decisions, while commercially fluent enough to negotiate scope with executives. This is a leadership and delivery role, but strong depth in AI system design is essential.

Work Hours

Hybrid remote with 15-20% On-Site

Hours per day: 2

Days per week: 5

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 that the engineering team can execute.

Solution Architecture

• Define target architectures 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 rationale 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 for probabilistic systems, including evaluation sets, accuracy and quality thresholds, latency budgets, and fallback behavior. AI features cannot be accepted through binary pass/fail criteria alone.
• Actively manage delivery risk, escalate issues early, and keep commitments realistic relative to engineering capacity.

Stakeholder and Governance Management

• Serve as the primary interface for business sponsors. Responsibilities include running discovery sessions, demos, steering reviews, and executive status reporting.
• Calibrate stakeholder expectations regarding what current AI can and cannot reliably do, and manage the gap between demonstrations and production performance.
• Guide solutions through security, legal, privacy, and responsible-AI reviews, while maintaining documentation of model use, data handling, and approved use cases.

Value Realization

• Define and track benefit metrics, including adoption, time saved, quality improvements, and costs avoided; 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 experience.
• 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-stage or ad hoc state.