Job Summary

The Principal AI Engineer will be an early member of a greenfield AI engineering organization focused on delivering innovative AI solutions that improve advisor productivity, automate workflows, reduce administrative overhead, and enhance client experiences. This hands-on role will shape technical direction, establish engineering best practices, and architect and deliver secure, scalable, enterprise-grade AI capabilities from concept through production. The role sits at the intersection of Generative AI, data engineering, cloud architecture, and product innovation, requiring the ability to identify high-value business problems and translate emerging AI capabilities into practical solutions with measurable business outcomes.

Key Responsibilities

• Design and deliver scalable, production-grade AI applications and distributed systems.

• Develop AI-powered solutions using large language models (LLMs), agent frameworks, and orchestration platforms.

• Design and implement Retrieval-Augmented Generation (RAG), semantic search, and enterprise knowledge systems using vector databases and retrieval frameworks.

• Build AI-powered capabilities for meeting preparation, research, call summarization, insight generation, workflow orchestration, knowledge retrieval, recommendations, decision support, and productivity enhancement.

• Develop workflow automation solutions leveraging enterprise data sources and communication channels.

• Collaborate with business leaders, product teams, architects, data scientists, engineers, and other stakeholders to identify high-value AI use cases.

• Translate business problems and requirements into secure, scalable, and practical AI solutions.

• Architect and build complex AI solutions from concept through production deployment.

• Establish and influence engineering best practices, technical standards, and architectural direction for AI solutions.

• Deploy and scale AI applications across cloud environments.

• Apply modern platform engineering and DevOps practices, including containers, Kubernetes, Infrastructure-as-Code, and cloud-native architectures.

• Ensure enterprise AI solutions meet security, reliability, scalability, and performance requirements.

• Drive initiatives across engineering, data, product, and business teams from concept to production.

• Evaluate emerging AI technologies and identify opportunities to apply them to business challenges.

• Deliver AI solutions that improve productivity, automate operational workflows, and create measurable business value.

Required Qualifications

• Bachelor's degree or equivalent experience with 8+ years of software engineering experience, or a Master's degree with 6+ years of software engineering experience.

• Proven experience designing and delivering scalable, production-grade software solutions and distributed systems.

• Deep expertise building AI-powered applications using large language models (LLMs), agent frameworks, and orchestration platforms such as OpenAI, Claude, Bedrock, LangChain, and LangGraph.

• Hands-on experience developing Retrieval-Augmented Generation (RAG) solutions, semantic search capabilities, and enterprise knowledge systems.

• Experience working with vector databases and retrieval frameworks.

• Strong full-stack engineering experience with modern languages and frameworks such as Python, TypeScript, Node.js, APIs, React, and Next.js.

• Hands-on experience deploying and scaling applications in 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, enterprise security, and reliability principles.

• Ability to collaborate effectively across engineering, data, product, and business teams.

• Strong problem-solving skills, technical judgment, and ability to drive complex initiatives from concept through production.

Preferred Qualifications

• Experience building enterprise-grade Generative AI and LLM applications.

• Experience developing AI agents and multi-step orchestration workflows.

• Experience integrating AI solutions with enterprise data sources and communication channels.

• Experience applying AI, automation, and advanced analytics to business workflow optimization.

• Experience working in greenfield AI engineering organizations or establishing technical direction and engineering practices.