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
The Full Stack AI Engineer is an early-career software engineering role focused on building and delivering full-stack applications with generative AI and agentic capabilities embedded into the solution architecture.
The role is designed for high-potential graduates and engineers with up to three years of experience who combine strong software engineering fundamentals with a different way of thinking about how AI can reshape enterprise workflows and applications.
This is not a traditional machine learning or data science role, and it is not about adding AI for AI's sake. The focus is on understanding business problems end to end and determining where large language models, agents, and AI-enabled workflows can materially improve how work gets done.
Working as part of a fully functioning engineering team, the Full Stack AI Engineer contributes across the delivery lifecycle, from understanding the problem and shaping the solution through development, deployment, and iteration.
Success in this role means writing high-quality code, learning quickly, contributing effectively within a team, and developing the judgment to build AI-enabled software that creates measurable value for clients and the business.
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-on 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.
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