Principal AI Engineer & Technical Lead

Jobtailor; Austin (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

LPL Financial is seeking a senior AI/ML software engineer to design, develop, and deploy production-grade AI/ML solutions that support business objectives and advisor experience.

You will build AI-powered applications using LLMs, generative AI, RAG architectures, and ML models; lead end-to-end lifecycle; collaborate with Wealth Management, Risk, Product, and Engineering to translate requirements into scalable solutions.

Qualifications

• Minimum of 8 years of software engineering, AI/ML engineering, or machine learning development experience.
• Proven track record of building and deploying production AI solutions in complex enterprise environments.
• Strong hands-on programming expertise in Python.

Responsibilities

• Design, develop, and deploy production-grade AI/ML solutions supporting business objectives and advisor experience.
• Architect and build AI-powered applications using LLMs, generative AI, and RAG architectures.
• Lead end-to-end software engineering lifecycle for AI solutions, including design, development, testing, deployment, monitoring, and optimization.
• Partner with Wealth Management, Operations, Risk, Marketing, Product, and Engineering teams to translate business requirements into scalable technical solutions.
• Develop and deliver AI-enabled products, platforms, and tools that improve advisor productivity and client experience.

Skills

Python Programming

AI/ML Engineering

NLP & ML

LLMs & Generative AI

RAG Architectures

APIs & Microservices

Cloud-native Architecture

Model Serving

Observability & Security

Education

Master's Degree in Computer Science

Bachelor's Degree in Computer Science

Tools

AWS Bedrock

CI/CD

Cloud-Native Apps

AI/ML Frameworks

Job description

LPL Financial is seeking a senior AI/ML software engineer to design, develop, and deploy production-grade AI/ML solutions that support business objectives and advisor experience.

You will build AI-powered applications using LLMs, generative AI, RAG architectures, and ML models; lead end-to-end lifecycle; collaborate with Wealth Management, Risk, Product, and Engineering to translate requirements into scalable solutions.