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

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 AIs 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 Ryans Key Results

This role operates in Ryans 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:

• Bachelors or masters 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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