Principal AI Engineer ($214,100 - $267,400)

Irvine Company•Irvine, CA

34d•$214,100 - $267,400•Onsite

About The Position

Lead the design and implementation of advanced AI solutions, primarily utilizing LLMs for natural-language interaction with enterprise data. This Principal Engineer role involves designing and building production AI applications (including conversational agents and RAG systems) to turn business questions into actionable insights. Expertise in modern LLM framework, embeddings, chunkings, and enterprise integration patterns is required to deliver intelligent, scalable solutions. This client-facing position necessitates communicating complex AI system behaviors to both technical and business leadership.

Requirements

3+ years of hands-on experience with LLMs, AI agents, or applied AI systems
Expertise in Python development, async programming patterns, and modern web frameworks
Proven experience developing RAG applications, including embedding strategies, chunking approaches, and retrieval optimization
Hands-on experience with vector databases and graph databases for semantic search and knowledge representation
Experience with multiple LLM providers (Gemini, Claude, LLaMA) and understanding of cost vs. capability tradeoffs
Experience with enterprise application integration, including defining integration architecture, data flows, and interface design
Strong understanding of SOA, microservices, or similar architectural patterns
Experience with CI/CD pipelines and infrastructure as code for deploying AI applications
Demonstrated ability to communicate with client leadership and translate business requirements into technical solutions
Experience with fine-tuning or adapting foundation models for domain-specific applications
Experience balancing feature development with technical debt management to deliver business value while maintaining quality

Nice To Haves

Experience with AI/ML agent orchestration frameworks such as Google ADK, LangChain, or similar tools
Experience with multi-agent architectures and workflow orchestration patterns
Experience with LLM evaluation methodologies, including metrics design, hallucination detection, and A/B testing for prompt optimization
Understanding of context engineering principles, including dynamic context assembly, tool result formatting, and conversation state management
Experience with Snowflake or BigQuery, including query optimization and connection management
Experience managing data systems, including persistence selection, schema design, and read/write tradeoffs
Familiarity with MLOps patterns and model deployment pipelines

Responsibilities

Lead the end-to-end development of advanced AI solutions, including agentic systems, RAG pipelines, and multi-agent workflows
Design and implement embedding and chunking strategies essential for effective Retrieval-Augmented Generation (RAG) across enterprise data
Create integrations to connect AI agents with enterprise data systems, APIs, and services
Develop robust, production-grade asynchronous Python applications with proper error handling and session management
Implement vector database and graph database solutions for semantic search and knowledge representation
Establish agentic architecture, including data flows, interfaces, and mechanisms across all enterprise systems
Deploy and manage AI agents on cloud platforms using CI/CD pipelines, infrastructure as code, and managed AI services
Implement monitoring, logging, and observability for agent interactions to guarantee system reliability and support debugging
Develop context engineering strategies that assemble dynamic context for consistent and accurate Large Language Model (LLM) behavior
Engage with clients to translate business requirements into AI solution architectures, and balance feature development with technical debt management