AI Delivery Lead Architect

Overview:

Engagement Type

Contract

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.Required/Desired Skills

Skill Required/Desired Amount of Experience - Prior hands-on engineering or data background. Required - Experience with Azure AI Foundry, AWS Bedrock, Google Vertex AI, or comparable enterprise AI platforms. Required - Familiarity with AI governance frameworks. Required - Experience managing vendor relationships and negotiating commercial terms. Required - Product management experience or formal certification in Agile, PMP, or an architecture framework such as TOGAF. Required - Experience building an AI delivery function from an early or ad hoc state. Required

Skills:

- 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.