This role pays less than
66%
of similar roles. Most pay
$192,452–$255,250
— the shaded band above.
At the midpoint, this role pays about
$208k
versus about
$224k
for comparable roles.
Based on 239 similar postings.
Employer
About LPL Financial
LPL Financial is the largest independent broker-dealer in the United States, providing brokerage and investment advisory services to independent financial advisors and financial institutions. Industry: Financial Services & Wealth Management
LPL Financial currently has
59 open roles
on FindRole.
Listed pay typically runs
$140,959–$234,882
across 54 roles with salary data.
The Principal AI Architect is responsible for designing, building, and governing a centralized AI Hub platform to enable business domains to securely develop and operate AI-powered applications. This role involves defining end-to-end architecture for model access, agent orchestration, prompt management, and data access layers while ensuring security, compliance, and observability across the enterprise. The architect will lead experimentation, conduct technical spikes, and manage MLOps and LLMOps capabilities to transition siloed solutions into a unified ecosystem. Key technologies include Python, Java, AWS services, Kubernetes, Bedrock, DynamoDB, and vector databases. The role addresses the challenge of creating a scalable, governed infrastructure for Generative AI, Agentic AI, RAG, and Multi-Agent solutions while enforcing data governance and security-by-design principles to ensure responsible AI adoption across various internal business units.
Design and govern the centralized AI Hub platform to provide shared capabilities like model access, agent orchestration, and prompt management.
Define reference architectures for Generative AI, Agentic AI, RAG, and Multi-Agent solutions across the enterprise.
Build and manage core platform services including LLM gateways, tool registries, and audit monitoring systems.
Design the data access layer to provide secure, scalable connections to structured and unstructured data sources.
Lead experimentation and proof-of-concept efforts to evaluate emerging AI models and translate them into production-ready capabilities.
Implement MLOps and LLMOps capabilities for the automated deployment, monitoring, and lifecycle management of AI models.
Embed security-by-design principles including identity propagation, PII protection, and AI guardrails into all platform components.
Establish standards for AI observability, usage analytics, cost management, and compliance reporting across the organization.
What we're looking for
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 of experience architecting and implementing AI/ML systems and data access layers including building or configuring platform services.
5+ years of experience and deep understanding of cloud-native architectures, APIs, microservices, event-driven systems, and scalable distributed applications.
Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.
Strong expertise with AWS services, Kubernetes/EKS, API Gateway, Bedrock, DynamoDB, and enterprise integration patterns (preferred).
Experience implementing AI governance, security, compliance, observability, and MLOps/LLMOps capabilities (preferred).
Proficiency in Python, Java, APIs, containers, Infrastructure-as-Code, and modern DevSecOps practices (preferred).
Agentic AI
Distributed Systems
Identity and Access Management
OAuth
OIDC
SAML
OPA
Rego
Cedar
Kafka
Spark
Cloud-Native Services
Data Governance
Security Architecture