Full Stack AI Engineer

Overview

In this early-career role you will design, build, and deploy AI-enabled full-stack applications that integrate generative AI and agentic capabilities to improve enterprise workflows. You’ll work within a cross-functional engineering team, from discovery through deployment, focusing on measurable business value. You’ll balance speed with quality, mentored growth, and hands-on contribution to real client solutions. This role offers mentorship, autonomy, and a path to rapidly develop both software and applied AI skills in a vibrant culture.

Pay / Benefits

• Competitive Compensation and Benefits
• Reimbursement (Phone/Internet)
• Gym Membership or Equipment Reimbursement
• LinkedIn Learning Subscription
• Flexible Work Environment
• Tuition Reimbursement After One Year of Service

Responsibilities

• Collaborate with cross-functional teams to design and improve AI-enabled full-stack software
• Participate in client-facing conversations and explain technical concepts to diverse stakeholders
• Contribute to end-to-end delivery including discovery, design, development, testing, deployment, and iteration
• Write high-quality, maintainable code and ensure reliability and business impact of solutions
• Balance engineering trade-offs and select appropriate AI-assisted workflows
• Leverage feedback to iterate on deployed applications and improve adoption and performance
• Mentor and grow through active learning and knowledge sharing

Key requirements

• Bachelor in Computer Science
• 0–3 years of professional software engineering experience
• Strong software engineering fundamentals and ability to build useful software
• Proficiency in Python, TypeScript, or JavaScript
• Experience building full-stack applications and familiarity with frontend/backend technologies (e.g., React, Node/TypeScript, Python/FastAPI)
• Understanding of APIs, databases, Git, testing, debugging, and application architecture
• Exposure to cloud platforms (AWS/Azure/GCP) and AI-related tooling (LLM APIs, agent frameworks)
• Ability to explain technical decisions and trade-offs, with interest in AI-enabled enterprise workflows
• Comfort with AI-assisted coding tools (e.g., Claude Code, Codex)
• Learning agility
• Collaborative teamwork
• Effective communication to technical and non-technical stakeholders
• Full-stack development
• LLM APIs and agent-based workflows
• Retrieval systems and vector databases

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