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About the Role
The Full Stack AI Engineer is an early-career role focused on building and delivering full-stack applications with generative AI and agentic capabilities. This role is for high-potential graduates and engineers with up to three years of experience who combine strong software engineering fundamentals with innovative thinking about AI's impact on enterprise workflows.
Responsibilities
- 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, 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.
- 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.
- 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.
- 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.
- Writes high-quality, maintainable code that contributes to dependable software.
- 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.
Requirements
- 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.
- 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.
- 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.
Skills
- Generative AI
- Agentic capabilities
- Full-stack development
- Software engineering fundamentals
- Python
- TypeScript
- JavaScript
- React
- Node/TypeScript
- Python/FastAPI
- APIs
- Databases
- Git
- Testing
- Debugging
- Application architecture
- AWS
- Azure
- GCP
- LLM APIs
- Agent-based workflows
- Retrieval
- Vector databases
- LangChain
- LangGraph
- AutoGen
- Claude Code
- Codex
Location
- Hybrid
Work Type
- Flexible Work Environment
- Hybrid
Experience Level
- Early-career
- 0-3 years of professional software engineering experience
Education Level
- Bachelor’s or master’s degree in Computer Science, Engineering, AI/ML, or a related technical field, or equivalent relevant experience.
Benefits
- Competitive Compensation and Benefits
- Business Connectivity Reimbursement (Phone/Internet)
- Gym Membership or Equipment Reimbursement
- LinkedIn Learning Subscription
- Tuition Reimbursement After One Year of Service
About the Company
- Award-Winning Culture & Community Outreach
- Results-oriented and flexible culture, with a strong emphasis on engineering quality, ownership, and measurable outcomes.
- Active mentorship and structured opportunities for growth support continued development in both software engineering and applied AI.