Architect enterprise-scale Agentic and Generative AI systems, including orchestration frameworks, multi-agent workflows, retrieval-augmented generation (RAG), and autonomous task execution patterns.
Establish reference architectures for AI-first PDLC covering design, development, testing, deployment, monitoring, and risk controls.
Partner with product and engineering teams to embed AI capabilities into advisor- and client-facing systems, internal tooling, and operational workflows.
Lead the architectural design of model lifecycle pipelines, including training, fine-tuning, evaluation, and model governance.
Define the architecture, tools, and methods required for an AI-native delivery lifecycle—integrating prompting, agent design, model testing, AI risk/controls, and continuous learning loops.
Design integrations between AI systems, core platforms (ClientWorks, Account Lifecycle), and enterprise data ecosystems.
Ensure all AI solutions meet LPL’s regulatory and compliance obligations.

Requirements

2+ years experience architecting Generative AI, Agentic AI, and ML systems at enterprise scale.
6+ years experience with cloud-native architecture background (AWS preferred), including microservices, Kubernetes, and event-driven patterns.
5+ years experience with LLMs, vector databases, embeddings, evaluators, guardrails, fine-tuning, and orchestration.
8+ years experience in Python; proficiency in C#, Java, or TypeScript is a plus.
6+ years expertise with ML Ops including CI/CD for ML, model versioning, monitoring, automated evaluation.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Generative AIAgentic AIMachine LearningPythonC#JavaTypeScriptML OpsCI/CD for MLmodel versioning

Soft Skills

leadershipcollaborationcommunication