Principal AI Architect
- Compensation
- Not disclosed
- Location
- Fort Mill/Charlotte
- Work setup
- Not specified
- Seniority
- Principal
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Architecture
- Posted
Skills
Must have
- 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, integ
- 5+ years experience and deep understanding of cloud-native architectures, APIs, microservices, event-driven systems, platform engineering, and scalable distributed applications.
Full description
Where Ambition Meets Innovation
Build a career that matches all your initiative with an impressive dose of innovation. From cutting-edge resources and a collaborative environment to the freedom to make an impact and more, you’ll find the ingredients you need at LPL Financial to shape your success while helping clients pursue their financial goals.
Job Overview
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.
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.