Full Stack AI engineer

Quality IT Source, LLC; New York (NY); On-site; USD 140,000 - 180,000; Full time

Job summary

Quality IT Source, LLC is seeking a highly skilled software engineer in New York to tackle complex backend and frontend challenges. The role requires expertise across Java, Spring Boot, ReactJS, and TypeScript, with hands-on work across distributed systems and AI integrations.

Applicants should be comfortable designing event-driven architectures, leveraging Kafka, Redis and PostgreSQL, and applying AI-driven practices throughout the development lifecycle.

Qualifications

• Strong backend engineering experience with Java and Spring Boot.
• Strong front-end development experience with ReactJS and TypeScript.
• Experience designing event-driven and distributed architectures, including hands‑on work with Apache Kafka.
• Experience with Redis and PostgreSQL, including an understanding of caching, data modelling and performance considerations.
• Hands‑on experience with agentic AI, LLMs or generative AI, including agent orchestration and tool use.
• Experience applying AI within the software development lifecycle, including AI-assisted engineering or automated coding and testing practices.

Skills

Backend engineering

Frontend development

Distributed architectures

AI integration concepts

Software development lifecycle

Tools

Java

Spring Boot

ReactJS

TypeScript

Apache Kafka

Redis

PostgreSQL

Job description

• Strong backend engineering experience with Java and Spring / Spring Boot, ideally including practical experience with Spring AI or comparable AI integration frameworks.
• Strong front-end development experience with ReactJS and TypeScript, with the ability to deliver production-quality features across the full stack.
• Experience designing event-driven and distributed architectures, including hands‑on work with Apache Kafka and asynchronous messaging patterns.
• Experience with Redis and PostgreSQL, including an understanding of caching, data modelling, performance and reliability considerations.
• Hands‑on experience with agentic AI, LLMs or generative AI, including practical understanding of agent orchestration, sub‑agent patterns, tool use, iterative loops and production integration.
• Experience applying AI within the software development lifecycle, whether through agentic development workflows, AI-assisted engineering or automated coding and testing practices.