AI Delivery Lead Architect

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