Full Stack AI Engineer

Role overview

Responsibilities

• check_circle Works as part of a cross-functional engineering team to design, build, and improve AI-enabled full-stack software.
• check_circle Collaborates effectively with engineers, business professionals, and end users to understand requirements and deliver practical solutions.
• check_circle Participates in client-facing conversations where appropriate, communicating technical concepts clearly to both technical and non-technical stakeholders.
• check_circle Learns from more experienced team members and contributes knowledge, ideas, and emerging best practices back into the team.
• check_circle Uses feedback constructively and demonstrates strong learning agility in a rapidly evolving technical environment.
• check_circle Works with business professionals and customers to understand problems, workflows, and opportunities for improvement.
• check_circle Helps determine where generative AI or agentic approaches can create meaningful value, rather than applying AI where traditional software would be more appropriate.
• check_circle Contributes to the end-to-end delivery of AI-enabled applications, including discovery, solution design, development, testing, deployment, and iteration. Build full-stack applications that may incorporate large language models, agent-based workflows, retrieval, APIs, and other AI capabilities as part of the overall architecture.
• check_circle Supports the deployment and adoption of solutions used by real users in business and client environments.
• check_circle Considers the complete enterprise workflow when designing solutions, including users, data, integrations, business rules, human oversight, and failure scenarios.
• check_circle Writes high-quality, maintainable code that contributes to dependable software used by real users.
• check_circle Applies AI where it materially improves a process, user experience, decision, or business outcome.
• check_circle Thinks critically about when an agentic solution is appropriate and when deterministic software is the better choice.
• check_circle Contributes to solutions that address real-world problems and generate measurable value for clients and the business.
• check_circle Iterates on deployed applications based on user feedback, performance, reliability, and adoption.
• check_circle Balances speed, scope, and engineering quality to support effective delivery.
• check_circle Makes effective use of AI-assisted coding tools while maintaining ownership and understanding of the code produced.

Basic qualifications

• 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.

Preferred qualifications

• 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.

Benefits

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