Full Stack AI Engineer

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Why Ryan? Hybrid Work Options Award-Winning Culture Generous Personal Time Off (PTO) Benefits 14-Weeks of 100% Paid Leave for New Parents (Adoption Included) Monthly Gym Membership Reimbursement OR Gym Equipment Reimbursement Benefits Eligibility Effective Day One 401K with Employer Match Tuition Reimbursement After One Year of Service Fertility Assistance Program Four-Week Company-Paid Sabbatical Eligibility After Five Years of Service The Full Stack AI Engineer is a hybrid technical and client-facing role responsible for the design, development, and deployment of applications that embed generative AI. The position is grounded in full-stack engineering, including the ability to work across the full stack to design, build, and deliver complete applications. Further, it extends that foundation by integrating generative AI capabilities, including large language models, agents, and LLM-based workflows, as an integral component of the application architecture rather than a supplementary layer. The role is oriented toward delivering measurable business outcomes through robust, dependable software. This position is designed for engineers with strong full-stack fundamentals who are developing their expertise in applied generative AI. It is a generative-AI engineering role built upon full-stack fundamentals, which is distinct from a traditional machine-learning or data-science position. Working across the full delivery lifecycle: from problem discovery and system design through build, deployment, and iteration - this engineer combines strong full-stack execution with applied generative AI to deliver software that creates measurable value for clients and the business. The role calls for a strong engineering mindset, a customer orientation, and the ability to translate complex problems into AI-powered, full-stack software. Duties and Responsibilities, as they align to Ryan's Key Results: This role is managed in a results-oriented manner that reflects Ryan's flexible culture while holding a high bar for engineering rigor. In place of the heavier ceremony of traditional software development, the team operates on a lightweight, outcome-driven cadence in which engineers are trusted with meaningful ownership and the autonomy to choose appropriate tools, approaches, and AI workflows. That autonomy is paired with clear accountability: direction is set through defined outcomes and high technical standards rather than prescriptive process, and engineers are responsible for the quality, reliability, and business impact of what they deliver. The approach is supported by active mentorship and structured opportunities for growth, and success is measured not only by working software but by the measurable value it creates for clients and business. Delivers AI-powered, full-stack software solutions in coordination with end users and business professionals. Serves as a technical point of contact for clients, building strong relationships with both technical and non-technical stakeholders. Shares insights and best practices from client engagements to improve internal tools, playbooks, and direction. Client: Collaborates with business professionals and customers to understand their needs and challenges, and to help identify high-value use cases for AI-powered software. Contributes to the end-to-end delivery of AI-powered, full-stack software solutions, including discovery, scoping, system design, development, and rollout to real users. Builds and deploys full-stack applications with generative AI engineered into the software, such as LLM-based workflows and agent-based systems within client infrastructure. Works within client and business environments to support successful adoption, performance, and measurable business outcomes. Value: Delivers AI-powered software that serves real users, addresses real-world problems, and generates tangible ROI. Supports continuous improvement by iterating on deployed solutions and optimizing performance and user adoption. Balances speed, scope, and quality to ensure timely and effective delivery of high-impact projects. Makes extensive use of AI-assisted coding and engineering workflows to deliver scalable, performant software. Education and Experience: A bachelor's or master's degree in computer science, Engineering, AI/ML, or a related field (or equivalent relevant work experience). A foundation in software engineering, with exposure to machine learning or solutions engineering environments. Exposure to, or strong interest in, integrating generative AI including large language models and agent frameworks into applications that serve users. Familiarity with, or demonstrated interest in, building agent-based solutions using frontier language models. Client-facing or consulting-style experience is a plus. Computer Skills: We are largely framework-agnostic; what matters is engineering fundamentals and demonstrated full-stack ability. Proficiency in at least one general-purpose programmi

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