Principal AI Architect
$155,942–$259,869 year
On-siteAustin, Texas, United States or New York, United States
Job Summary
Architect and lead LPL's Enterprise AI Hub Platform, establishing a shared foundation for model access, agent orchestration, and prompt governance. Define reference architectures for Generative AI, Agentic AI, RAG, and Multi-Agent solutions, while designing core services including registries, gateways, and data access layers. Lead experimentation and proof-of-concept efforts to validate emerging models and translate them into production-ready capabilities. Embed security-by-design principles, ensure compliance with enterprise standards, and govern observability, cost management, and lineage. Collaborate with Enterprise Architecture, Engineering, Security, and Product teams to drive the transition from siloed solutions to a scalable, governed ecosystem. Mentor architects and engineers on best practices while influencing long-term AI strategy and technology investments.
Required Qualifications
- 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
- 5+ years experience and deep understanding of cloud-native architectures, APIs, microservices, event-driven systems, platform engineering, and scalable distributed applications
- Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field
Desired Qualifications
- 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
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