AI Delivery Lead Architect – Part time 

Client: Supreme Court of Ohio
Location: Ohio – Hybrid
Onsite Requirement: Approximately 20% onsite
Hours: 5–10 hours per week
Interview: In-Person
Contract: Long-Term Contract

Role Overview

The Supreme Court of Ohio is seeking an experienced AI Delivery Lead Architect to own the end-to-end journey from identifying business problems to delivering and driving adoption of AI products.

This role serves as the primary point of accountability between business stakeholders and engineering teams, responsible for shaping the AI roadmap, defining delivery requirements and success criteria, guiding solution architecture, managing delivery, and ensuring AI solutions generate measurable business value.

The ideal candidate will combine strong AI architecture and technical credibility with product leadership, delivery management, stakeholder engagement, commercial awareness, and responsible AI governance experience.

This is a leadership and delivery-focused role, but strong knowledge of AI system architecture and modern enterprise AI platforms is essential.

Key Responsibilities

Demand Shaping & Prioritization

• Lead intake and evaluation of AI use-case requests.

• Assess proposed AI initiatives based on:

  • Business value

  • Technical feasibility

  • Data readiness

  • Security and operational risk

  • Implementation complexity

• Maintain and prioritize an AI delivery roadmap.

• Translate ambiguous business problems into clearly defined requirements.

• Establish measurable success criteria and technical requirements that engineering teams can execute against.

• Prioritize AI initiatives based on business impact, feasibility, capacity, and risk.

AI Solution Architecture

• Define target architectures for enterprise AI products in partnership with senior engineers and technical teams.

• Design and evaluate:

  • AI agent architectures

  • Retrieval strategies

  • Model selection

  • Integration points

  • Data flows

  • AI application components

• Establish reusable architecture patterns and technical standards across the AI portfolio.

• Define and evaluate build-versus-buy decisions.

• Evaluate AI models, platforms, technologies, and vendors.

• Develop the technical business case supporting AI platform and technology selections.

• Ensure proposed architectures are scalable, secure, maintainable, and aligned with enterprise standards.

Delivery Ownership

• Own the complete AI product delivery lifecycle, including:

  • Scoping

  • Estimation

  • Sprint planning

  • Dependency management

  • Risk management

  • Release planning

  • Deployment

  • Hypercare

• Work closely with engineering teams to ensure delivery remains aligned with business objectives.

• Establish realistic delivery commitments based on engineering capacity and technical complexity.

• Identify and escalate delivery risks early.

• Define acceptance criteria appropriate for AI and probabilistic systems.

• Establish evaluation sets, quality thresholds, accuracy expectations, latency budgets, and fallback behavior.

• Ensure AI solutions are evaluated using appropriate quality and performance measures rather than relying solely on binary pass/fail criteria.

Stakeholder & Governance Management

• Serve as the primary interface between AI engineering teams and business sponsors.

• Lead:

  • Discovery sessions

  • Requirements workshops

  • Product demonstrations

  • Steering committee reviews

  • Executive status reporting

• Set realistic expectations regarding current AI capabilities and limitations.

• Manage the transition from AI prototypes/demos to production-ready solutions.

• Coordinate security, legal, privacy, and responsible AI reviews.

• Maintain documentation related to:

  • AI model usage

  • Data handling

  • Approved use cases

  • Governance requirements

  • Security and privacy considerations

• Ensure AI solutions align with organizational governance and responsible AI standards.

Value Realization & Adoption

• Define and track measurable AI business outcomes, including:

  • User adoption

  • Time savings

  • Quality improvements

  • Cost avoidance

  • Operational efficiency

• Measure delivered results against the original business case.

• Report AI initiative outcomes to business and executive stakeholders.

• Manage AI platform and inference costs.

• Support budget forecasting and per-workload cost attribution.

• Partner with enablement and change-management teams to drive adoption following launch.

• Identify opportunities to improve adoption and maximize business value.

Required Qualifications

• Prior hands-on engineering or data background.

• Experience with enterprise AI platforms such as:

  • Azure AI Foundry

  • AWS Bedrock

  • Google Vertex AI

  • Comparable enterprise AI platforms

• Strong understanding of AI solution architecture and delivery.

• Familiarity with AI governance frameworks.

• Experience managing vendor relationships and negotiating commercial terms.

• Product management or AI product delivery experience.

• Formal certification in one or more of the following is preferred:

  • Agile

  • PMP

  • TOGAF

  • Comparable product/architecture methodology

• Experience building or establishing an AI delivery function from an early-stage or ad hoc environment.

• Strong stakeholder management and executive communication skills.

• Ability to translate business problems into practical AI solutions.

• Strong understanding of AI risks, limitations, governance, security, privacy, and responsible AI practices.

Preferred Experience

• Experience delivering enterprise Generative AI, LLM, RAG, or agentic AI solutions.

• Experience with AI model evaluation and quality measurement.

• Experience managing AI platform/inference costs.

• Experience establishing AI roadmaps and portfolio prioritization.

• Experience with enterprise AI vendor evaluation and selection.

• Experience working in regulated or government environments.

• Experience leading cross-functional AI teams.

• Experience taking AI solutions from POC/prototype through production and adoption.

Core Skills

AI Delivery | AI Architecture | Generative AI | LLMs | AI Agents | RAG | Azure AI Foundry | AWS Bedrock | Google Vertex AI | AI Governance | Responsible AI | Product Management | Technical Architecture | Agile | PMP | TOGAF | Vendor Management | Stakeholder Management | AI Evaluation | AI Cost Management | Enterprise AI

Work Arrangement

• Hybrid

• Approximately 20% onsite

• 5–10 hours per week

• In-person interview required

• Candidate must be able to meet the required onsite schedule.

• Hands-on engineering or data background

• Enterprise AI platform experience

• Azure AI Foundry / AWS Bedrock / Google Vertex AI or equivalent

• AI solution architecture experience

• AI governance experience

• Vendor management experience

• Commercial negotiation experience

• Product management / AI delivery experience

• Agile / PMP / TOGAF certification or equivalent

• Experience building an AI delivery function

• GenAI / LLM / RAG experience preferred

• AI agent architecture experience preferred

• AI evaluation experience preferred

• AI cost management experience preferred

• Strong executive/stakeholder communication

• Candidate accepts hybrid work with approximately 20% onsite

• Candidate is available for 5–10 hours/week

• Candidate accepts in-person interview

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