Principal AI Engineer

Job Title

Example Use Cases Representative

Representative initiatives may include developing AI-powered capabilities that improve advisor effectiveness and client engagement, such as:
Automated meeting preparation and research Intelligent call summarization and insight generation Automated follow-up and workflow orchestration Knowledge retrieval and recommendation systems AI-assisted decision support and productivity tools Workflow automation leveraging enterprise data sources and communication channels

The Skills You Bring Bachelor's degree or equivalent experience with 8+ years of software engineering experience, or a Master's degree with 6+ years of experience. Proven experience designing and delivering scalable, production-grade software solutions and distributed systems. Deep expertise building AI-powered applications utilizing large language models (LLMs), agent frameworks, and orchestration platforms (e.g., OpenAI, Claude, Bedrock, Lang Chain, Lang Graph). Experience developing Retrieval-Augmented Generation (RAG) solutions, semantic search capabilities, and enterprise knowledge systems leveraging vector databases and retrieval frameworks.

Strong full-stack engineering background with modern languages and frameworks such as Python, Type Script, Node.js, APIs, React, and Next.js. Hands-on experience deploying and scaling applications within cloud environments such as AWS, Azure, or Google Cloud. Experience with modern platform engineering and Dev Ops practices, including containers, Kubernetes, Infrastructure-as-Code, and cloud-native architectures. Strong understanding of software architecture, design patterns, security, and reliability principles for enterprise-scale applications.

Excellent problem-solving skills, sound technical judgment, and a passion for building innovative solutions. Ability to collaborate effectively across engineering, data, product, and business teams while driving initiatives from concept to production. Ultimately, I need a hands-on Principal Engineer who combines GenAI/LLM expertise, modern data engineering, and cloud architecture skills to identify business problems and deliver production AI solutions that improve advisor productivity, automate workflows, and create measurable business outcomes.