Principal AI Architect

Experience: 15+ Years

Visa: EAD, L2, E2, TN, (No H1B & OPT)

Summary

The Associate Principal, AI Optimization & Delivery – Solution Architecture serves as the technical cornerstone of Client’s AI Optimization & Delivery organization. As a principal individual contributor with a greenfield mandate, this role serves as the primary technical design authority for AI Optimization & Delivery.

The position partners with AI Optimization & Delivery leadership, Enterprise Architecture, and delivery pods to ensure Client’s AI ecosystem integrates cohesively with the enterprise and supports adoption across distributed engineering teams.

The immediate focus is to bring architectural clarity to existing pilot initiatives by designing blueprints for an agentic component layer, establishing technical exit criteria for the sandbox-to-production graduation process, and laying the foundation for a managed AI Internal Developer Platform (IDP). Over time, this role will architect an ecosystem that enables Client’s engineering teams to deliver AI-powered solutions at scale, accelerate delivery velocity, and prevent architectural debt.

This role combines hands-on technical design with systems-level thinking. The ideal candidate can objectively evaluate emerging frameworks, serve as a technical design authority for delivery pods, and translate regulatory and business requirements into secure, scalable platform capabilities.

Responsibilities

Technical Architecture and Standards

· Establish architectural standards and design patterns for LLM-enabled workflows, agentic systems, and multi-agent orchestration.

· Define hybrid system patterns that balance LLM-based judgment with deterministic code for high-confidence and compliance-sensitive operations.

· Architect the initial design and governing standards for the platform component layer, including shared libraries, reusable agent templates, retrieval-augmented generation (RAG) pipelines, vector databases, and hybrid search patterns.

· Establish contribution models and reusable patterns that engineering squads can adopt, extend, and maintain.

· Define the AI Bill of Materials (AIBOM) approach, including model provenance tracking, third-party model risk assessment, and version governance.

· Establish observability standards that support telemetry, reasoning traceability, operational monitoring, and auditability.

· Design routing architectures that optimize latency and cost, including semantic caching, model fallback strategies, and intelligent workload distribution between small language models (SLMs) and frontier large language models (LLMs).

Platform Architecture and Governance

· Define technical exit criteria and architectural standards for a governed sandbox-to-production graduation process.

· Partner with AI Operations (AIOps) and pilot teams to transition successful initiatives into governed platform environments.

· Architect and maintain data models for enterprise agent registries and context-management strategies, including memory architecture and resource-connectivity patterns.

· Lead technical evaluations of AI technologies, platforms, and protocols, documenting architectural rationale and supporting vendor-agnostic decision-making.

· Architect data privacy and security boundaries tailored to healthcare data, including protected health information (PHI) and personally identifiable information (PII).

· Ensure RAG, memory, and context-management systems are designed to comply with HIPAA requirements and clinical data-sharing agreements.

Platform Evolution and Maturity

· Define year-one technical milestones for Client’s AI maturity model.

· Maintain awareness of emerging AI frameworks, tools, and architectural patterns, translating relevant developments into prioritized platform-evolution recommendations.

· Establish evaluation methodologies and validation frameworks, including LLM-as-judge calibration, prompt-testing criteria, regression testing, and other evaluation practices for probabilistic systems.

· Integrate evaluation and validation capabilities into engineering and deployment pipelines.

· Partner with active pilot teams to identify reusable architectural patterns and continuously strengthen the platform component layer.

· Design rate-limiting, token-throttling, and cross-charge model cost-allocation architectures.

· Deliver instrumentation that enables per-workflow cost visibility, governance, and FinOps reporting.

Leadership and Delivery Enablement

· Serve as the technical design authority for delivery pods and own solution blueprints that align with AI-DLC gates.

· Eliminate architectural ambiguity early in the delivery lifecycle to accelerate execution and prevent technical debt.

· Partner with Enterprise Architecture to integrate the AI stack into Client’s broader enterprise architecture blueprint.

· Represent and maintain Client’s AI architecture position while coordinating with Enterprise Architecture as an integration partner.

· Collaborate with Engineering and Operations to design deployment automation and embed governance through policy-as-code frameworks and other programmatic controls.

· Partner with Security, Clinical, Data, and Product teams to translate regulatory, operational, and domain requirements into native platform capabilities.

· Present architecture positions, technical evaluations, and recommendations to AI Optimization & Delivery leadership and cross-functional stakeholders.

· Own and maintain a library of reference architectures, technical standards, and decision trees that enable distributed engineering teams to build confidently without requiring architectural review for every engagement.

Qualifications

· 12 or more years of experience in enterprise software architecture, platform architecture, AI/ML systems, data architecture, or enterprise platform delivery, including experience operating at enterprise scale.

· At least 2 years of dedicated, hands-on experience delivering LLM orchestration and agentic systems in production, including multi-agent orchestration, context management, and tool connectivity.

· Strong systems-thinking skills, with the ability to evaluate emergent behavior, failure modes, and downstream architectural consequences before solutions are implemented.

· Demonstrated ability to make, communicate, and defend principled architectural decisions.

· Experience designing AI/ML platform infrastructure, including cloud AI platform integration, LLMOps/MLOps tooling, deployment pipelines, observability, and runtime monitoring.

· Experience with LLM evaluation practices, including LLM-as-judge calibration, regression pipelines, prompt testing, and validation of probabilistic systems.

· Strong written and verbal communication skills, with the ability to create architecture decision records, technical standards, reference architectures, and other technical documentation.

· Demonstrated ability to collaborate effectively with stakeholders across Engineering, Security, Compliance, Data, Product, and business functions.

· Experience working in regulated data environments, including data governance, audit readiness, privacy boundaries, and compliance controls.

Preferred Experience

· Deep familiarity with healthcare data environments, including HIPAA, PHI handling, and clinical data-sharing constraints.

· Experience building or governing enterprise AI/ML platforms, including agent registries, model-provenance tracking, AIBOM concepts, and AI governance frameworks.

· Demonstrated experience building reusable platform components, such as shared libraries, templates, run patterns, reference architectures, or developer-enablement assets.

· Experience creating platforms and tools that accelerate downstream engineering teams while minimizing unnecessary dependencies.

· Demonstrated ability to earn adoption through product quality, technical credibility, and peer influence, particularly in horizontal platform roles where influence precedes formal authority.