Principal AI Architect

LPL Financial•Fort Mill, SC

1d•$155,942 - $259,869

About The Position

The Principal Architect, AI is responsible for designing, building, and governing LPL's centralized AI Hub platform, enabling business domains to securely develop, deploy, and operate AI-powered applications using a shared enterprise AI foundation. This role defines the end-to-end enterprise AI platform architecture, including model access, agent orchestration, prompt and tool governance, data access, AI security, observability, compliance, and platform services. The architect partners with Enterprise Architecture, AI Engineering, Security, Infrastructure, Data, and Product teams to accelerate responsible, scalable, and compliant AI adoption across the organization.

Requirements

10+ years of experience in Enterprise Architecture, Software Engineering, Platform Architecture, or Distributed Systems.
3+ years of experience designing and implementing AI, Machine Learning, Generative AI, or Agentic AI platforms.
3+ years experience architecting and implementing AI/ML systems and data access layers — not limited to high-level design — including direct experience building or configuring platform services, integrations, and POCs.
5+ years experience and deep understanding of cloud-native architectures, APIs, microservices, event-driven systems, platform engineering, and scalable distributed applications.
Ability to solve complex enterprise-scale business and technology challenges.
Deep expertise in AI platform architecture, data access architecture, cloud architecture, and enterprise integration patterns.
Strong strategic thinking with the ability to balance immediate delivery needs and long-term platform vision.
Excellent verbal and written communication skills, capable of influencing technical and executive stakeholders.
Strong leadership, mentoring, collaboration, and architecture governance capabilities.
Passion for advancing scalable, secure, compliant, and enterprise-grade AI adoption across the organization.

Nice To Haves

Strong expertise with AWS cloud services, Kubernetes/EKS, API Gateway, Bedrock, DynamoDB, security frameworks, and enterprise integration patterns.
Experience designing and implementing data access layers, data abstraction patterns, and secure data integration for AI/ML workloads.
Experience implementing AI governance, security, compliance, observability, and responsible AI controls.
Strong knowledge of Generative AI, Agentic AI, MCP (Model Context Protocol), A2A (Agent-to-Agent), RAG, Vector Databases, and AI orchestration frameworks.
Experience with MLOps, LLMOps, model registries, prompt management, deployment automation, model lifecycle management, and AI monitoring platforms.
Demonstrated experience leading experimentation and proof-of-concept initiatives, evaluating new AI technologies, and driving them to production.
Proficiency in Python, Java, APIs, containers, Infrastructure-as-Code, and modern DevSecOps practices.
Proven ability to influence executive stakeholders, drive strategic initiatives, mentor teams, and lead cross-functional architecture programs.
Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.

Responsibilities

Architect and lead LPL's Enterprise AI Hub Platform, providing centralized AI capabilities across business domains.
Establish a shared AI platform for model access, agent orchestration, prompt management, tool integration, governance, and observability.
Define reference architectures for Generative AI, Agentic AI, RAG, MCP, and Multi-Agent solutions.
Design and govern core platform services including: Model, Agent, Prompt, and Tool Registries; LLM Gateway and AI Access Layer; MCP and A2A Gateways; Audit, Monitoring, and Compliance Services.
Design and govern the Data Access Layer for the AI platform, including secure and scalable access to structured and unstructured data sources, data abstraction and query interfaces, connection to vector databases and knowledge stores, data lineage, and enforcement of data governance, privacy, and permissioning policies across AI workloads.
Lead experimentation and proof-of-concept (POC) efforts to evaluate emerging AI models, frameworks, and architectural patterns; run technical spikes to validate feasibility, performance, and scalability; and translate successful POCs into production-ready platform capabilities.
Lead implementation of AI Platform Engineering, MLOps, and LLMOps capabilities, including onboarding, deployment, monitoring, and lifecycle management.
Define standards for agent onboarding, agent communication, AI interoperability, and model governance.
Embed security-by-design principles including identity propagation, RBAC, PII protection, AI guardrails, content safety, and auditability.
Ensure compliance with enterprise security, risk, regulatory, and responsible AI standards.
Architect and govern AI observability, usage analytics, lineage, cost management, and compliance reporting.
Evaluate and recommend enterprise technologies for foundation models, AI gateways, agent frameworks, MCP, vector databases, and AI governance platforms.
Drive the transition from siloed AI solutions to a scalable, governed enterprise AI ecosystem.
Collaborate with Enterprise Architecture, Engineering, Security, Data, and Product teams to align AI strategy with business objectives.
Influence AI platform roadmaps, technology investments, governance frameworks, and long-term AI strategy.
Mentor architects, engineers, and AI teams on enterprise AI architecture and platform best practices.