Full Stack AI Engineer

Why Ryan?

• Competitive Compensation and Benefits
• Business Connectivity Reimbursement (Phone/Internet)
• Gym Membership or Equipment Reimbursement
• LinkedIn Learning Subscription
• Flexible Work Environment
• Tuition Reimbursement
• After One Year of Service
• Accelerated Career Path
• Award-Winning Culture & Community Outreach

#Li-hybrid #LI-DR1 Ryan offers outstanding opportunities to work in a dynamic, rapidly expanding tax services firm serving the world’s most respected Global 5000 companies. Our innovative work environment, accelerated growth path for high performers, competitive benefits package, and outstanding earning potential provide the most rewarding career experience available in the industry. Ryan employees are given the freedom to do their best work in the way they work best. With a clear understanding of expectations and accountabilities, our employees are given ownership of their time and flexibility to meet the demands of their professional and personal lives.

Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States and with Ryan LLC (i.e., H1-B visa, F-1 visa (OPT), TN visa or any other non-immigrant status). Job duties related to this role are to be conducted in a manner that adheres to privacy laws, as well as follows internal governance related to protecting confidential information and trade secrets, and to securing data and company records.

Duties and responsibilities, as they align to Ryan's Key Results

This role operates in Ryan's results-oriented and flexible culture, with a strong emphasis on engineering quality, ownership, and measurable outcomes. Engineers are trusted to choose appropriate tools, approaches, and AI-assisted workflows rather than follow heavy development processes. That autonomy is paired with clear accountability for the quality, reliability, and business impact of what they deliver. The role is intended for engineers early in their careers. Success is not measured by years of experience or by knowledge of a particular AI framework. It is measured by strong engineering fundamentals, learning agility, quality of thinking, and the ability to contribute working code to AI-enabled solutions. Active mentorship and structured opportunities for growth support continued development in both software engineering and applied AI.

People: Works as part of a cross-functional engineering team to design, build, and improve AI-enabled full-stack software. Collaborates effectively with engineers, business professionals, and end users to understand requirements and deliver practical solutions. Participates in client-facing conversations where appropriate, communicating technical concepts clearly to both technical and non-technical stakeholders. Learns from more experienced team members and contributes knowledge, ideas, and emerging best practices back into the team. Uses feedback constructively and demonstrates strong learning agility in a rapidly evolving technical environment.

Client: Works with business professionals and customers to understand problems, workflows, and opportunities for improvement. Helps determine where generative AI or agentic approaches can create meaningful value, rather than applying AI where traditional software would be more appropriate. Contributes to the end-to-end delivery of AI-enabled applications, including discovery, solution design, development, testing, deployment, and iteration. Builds full-stack applications that may incorporate large language models, agent-based workflows, retrieval, APIs, and other AI capabilities as part of the overall architecture. Supports the deployment and adoption of solutions used by real users in business and client environments. Considers the complete enterprise workflow when designing solutions, including users, data, integrations, business rules, human oversight, and failure scenarios.

Value: Writes high-quality, maintainable code that contributes to dependable software used by real users. Applies AI where it materially improves a process, user experience, decision, or business outcome. Thinks critically about when an agentic solution is appropriate and when deterministic software is the better choice. Contributes to solutions that address real-world problems and generate measurable value for clients and the business. Iterates on deployed applications based on user feedback, performance, reliability, and adoption. Balances speed, scope, and engineering quality to support effective delivery. Makes effective use of AI-assisted coding tools while maintaining ownership and understanding of the code produced.

Education and Experience:

Bachelor’s or master’s degree in Computer Science, Engineering, AI/ML, or a related technical field, or equivalent relevant experience. Suitable for recent graduates and candidates with approximately 0-3 years of professional software engineering experience. Strong foundation in software engineering, demonstrated through professional work, internships, university projects, personal projects, open-source contributions, hackathons, or equivalent technical experience. Evidence of interest in and hands‑on exploration of generative AI, large language models, or agentic systems. Commercial generative AI experience is not required. Ability to explain technical decisions, trade-offs, and personal contribution to projects in detail. Strong interest in understanding how AI can change enterprise workflows and software architecture, rather than simply adding AI features to existing applications.

Computer Skills:

We are largely framework-agnostic. What matters most is strong engineering fundamentals, the ability to learn quickly, and evidence that you can build useful software. Proficiency in at least one general-purpose programming language, such as Python, TypeScript, or JavaScript. Experience building full-stack applications through professional work, internships, academic projects, or independent development. Strong understanding of core software engineering concepts, including APIs, databases, Git, testing, debugging, and application architecture. Familiarity with frontend and backend development using technologies such as React, Node/TypeScript, Python/FastAPI, or similar. Conceptual understanding of cloud platforms such as AWS, Azure, or GCP; hands‑off deployment experience is beneficial but not required. Exposure to generative AI application development, including LLM APIs, agentic workflows, retrieval, vector databases, or agent frameworks such as LangChain, LangGraph, or AutoGen. Familiarity with AI‑assisted coding tools such as Claude Code, Codex, or similar, with the ability to validate and understand AI-generated code.

Equal Employment Opportunity/Affirmative Action/Accommodation

Ryan, LLC is an equal opportunity employer and is committed to compliance with all applicable laws prohibiting employment discrimination. It is our policy to take all employment actions and make all employment decisions without regard to race, color, religion, creed, gender, sex (including pregnancy), affectional or sexual orientation, gender identity or expression, national origin, ancestry, age, marital status, citizenship status, genetic predisposition or carrier status, disability, military status, status as a disabled or other protected veteran or any other protected status under applicable law. It is Ryan's policy to make reasonable accommodation for qualified individuals with disabilities.

*Notice to Canada Candidates – In accordance with the Accessibility for Ontarians with Disabilities Act (AODA) and the Canadian Human Rights Act, Ryan ULC will provide accommodation, accessible formats and communication supports for the interview upon request.Ryan welcomes and encourages applications from people with disabilities*.

Click here to view the entire EEO poster and supplement.