Principal AI Architect

CVS Health•Work At Home-Colorado, CO

4d•$144,200 - $288,400

About The Position

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. The Principal AI Architect is the senior individual contributor accountable for AI architecture across Aetna Technology. This role sets the technical direction for how AI capabilities are built, served, orchestrated, and governed — including the multi-model platform and spanning generative and agentic workloads. The Principal AI Architect defines the AI reference architectures and paved road that teams build on, sets the pattern for agentic systems, and serves as Aetna Technology's deepest technical authority on AI — driving outcomes through design authority and influence rather than direct reports.

Requirements

12+ years in technology, with deep, hands-on experience in AI/ML platform and solution architecture at Aetna Technology scale.
Expert-level command of model serving, multi-model orchestration, retrieval, and evaluation on cloud platforms (AWS primary; GCP/Azure familiarity preferred).
Demonstrated ownership of AI reference architectures and paved-road / golden-path patterns for model and agent development.
Hands-on experience designing agentic systems — orchestration, tool/skill layers, and the integration patterns (API, MCP) behind them.
Strong grounding in responsible AI, model evaluation, and guardrail design.
Track record of driving technical outcomes through influence and design authority as an individual contributor.
Bachelor's degree in Computer Science, Engineering, or a related field or equivalent experience.

Nice To Haves

Experience in healthcare, health insurance, or similarly regulated industries.
Familiarity with FinOps and cost-aware AI architecture, able to tie design decisions to dollar impact.
Experience instrumenting AI ROI and adoption metrics for executive audiences.
Experience with infrastructure-as-code and automated policy/guardrail enforcement.

Responsibilities

Own the Aetna Technology AI reference architectures — model serving, multi-model orchestration, retrieval, and evaluation — across AWS-primary and multicloud infrastructure.
Define the agentic architecture: agent orchestration as the enforced contract, domain agents scoped by journey and business context, and a reusable skills layer where reusability actually lives.
Set the integration default — API-first, with MCP adopted by exception — and the criteria that govern that exception.
Design and maintain the paved road for AI development — golden patterns for building, serving, and evaluating models and agents so teams ship without re-solving solved problems.
Embed guardrails, evaluation, and safety controls into the paved road so responsible-AI practice is inherited by default, not bolted on per team.
Define the model evaluation and benchmarking discipline — how models are compared, selected, and re-tested as the landscape shifts.
Establish controls for regulated and sensitive data in AI workloads (including PHI) — access, grounding, and blast-radius reduction across agents and models.
Define the instrumentation that ties AI investment to measurable outcomes — the ledger, labor-redeployment signals, and usage telemetry — surfaced in concrete dollar terms.
Partner with platform and finance teams on cost-aware AI architecture so unit economics are a first-class design constraint.
Author the AI standards enforced as automated gates and paved-road defaults, not as a review queue.
Contribute to architecture governance — reference architectures, technology radar input across a fast-moving model landscape, and design reviews — in partnership with Enterprise Architecture.
Evaluate emerging models, frameworks, and agentic tooling and make directional recommendations on adoption.
Translate AI architecture trade-offs into clear, decision-oriented narratives for senior technology leaders and peer architects.
Coach and raise the bar for Solution Architects and senior engineers through design reviews and system-level reasoning — without direct reporting relationships.
Act as a credible technical partner to strategic AI vendors and internal platform teams.