Principal AI Engineer 181797

Example Use Cases

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, LangChain, LangGraph).
  




• 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, TypeScript, 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 DevOps 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.