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 5+ years of software engineering experience. Proven experience designing and delivering scalable, production-grade software solutions and distributed systems. Strong experience using LLMs and Generative AI technologies to solve real business challenges 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. Top 4 Areas of Expertise 1. Backend & Full Stack Engineering (25%) Strong backend engineering background (role is heavily backend-focused) Hands on experience with programming languages such as Java, Node.js, Python Familiarity with modern web application architectures API design, development, and integration expertise 2. AI & LLM Engineering (25%) Experience leveraging Large Language Models (LLMs) to solve business problems Strong understanding of prompt engineering, prompt management, and GenAI solution design Experience working with GenAI developer tools such as: GitHub Copilot, Gemini, Claude Familiarity with AI strategy, implementation, and scaling AI-driven solutions 3. Data Platform Engineering (25%) Strong data engineering experience Experience with 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 4. Cloud Architecture, Scale & Security (25%) Cloud-native development experience Kubernetes and containerized application deployment Experience with deployment patterns and scalable system design Understanding of secure, enterprise-grade architecture Ultimately, I need a hands-on Senior AI Engineer who combines GenAI/LLM experience, strong software and data engineering fundamentals, and cloud platform expertise to design, build, and deploy AI solutions that solve real business challenges, enhance advisor efficiency, and automate key processes at scale.