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Job Summary
We are seeking a hands-on Principal Full Stack AI Engineer to help build a new AI engineering organization focused on delivering innovative AI solutions that address business challenges, improve advisor productivity, streamline workflows, and enhance client experiences. The role will combine GenAI and LLM engineering, full-stack development, data engineering, cloud architecture, and platform engineering to design, build, and scale secure, reliable, enterprise-grade AI solutions from concept through production.

Key Responsibilities
• Architect and develop production-grade AI-powered applications and distributed systems.
• Identify high-value business use cases and translate emerging AI capabilities into practical enterprise solutions.
• Leverage LLMs and Generative AI technologies to address business challenges and improve workflows.
• Design and implement AI-powered capabilities such as automated meeting preparation, call summarization, knowledge retrieval, recommendations, decision support, and workflow automation.
• Develop scalable backend and full-stack solutions using modern programming languages, frameworks, and APIs.
• Design, develop, and integrate APIs supporting AI-powered applications and enterprise workflows.
• Build and support data pipelines, data stores, orchestration frameworks, and distributed systems for AI-enabled applications.
• Deploy and scale AI and machine learning solutions in production environments.
• Develop and maintain cloud-native applications using containerized architectures and Kubernetes.
• Apply Infrastructure-as-Code and modern DevOps practices to support scalable and reliable deployments.
• Establish and promote engineering best practices, technical standards, and scalable solution architectures.
• Apply security and reliability principles to enterprise-scale applications and AI solutions.
• Collaborate with engineering, data, product, architecture, and business teams to drive initiatives from concept through production.
• Evaluate AI technologies and developer tools and contribute to AI strategy, implementation, and scaling initiatives.
• Provide technical leadership and influence engineering direction within the AI organization.

Required Qualifications
• 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.
• Strong experience using LLMs and Generative AI technologies to solve business challenges.
• Strong full-stack engineering background with modern technologies such as Python, TypeScript, Node.js, APIs, React, and Next.js.
• Strong backend engineering experience with programming languages such as Java, Node.js, or Python.
• Experience with API design, development, and integration.
• 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, security, and reliability principles for enterprise-scale applications.
• Strong data engineering experience, including data pipelines, data stores, orchestration frameworks, and distributed systems.
• Understanding of machine learning workflows and AI-enabled applications.
• Experience deploying and scaling AI/ML solutions in production environments.
• Excellent problem-solving skills and sound technical judgment.
• Ability to collaborate effectively across engineering, data, product, and business teams while driving initiatives from concept to production.

Preferred Qualifications
• Experience with modern web application architectures.
• Experience with GenAI developer tools such as GitHub Copilot, Gemini, or Claude.
• Strong understanding of prompt engineering, prompt management, and GenAI solution design.
• Familiarity with AI strategy, implementation, and scaling AI-driven solutions.
• Experience with cloud-native development and scalable system design.
• Experience with Kubernetes and containerized application deployment.