Java Fullstack Developer

Overview

Remote
$30 - $60
Contract - Independent
Contract - W2

Skills

API
Amazon Web Services
AngularJS
Artificial Intelligence
Cloud Computing
Collaboration
Communication
Continuous Delivery
Continuous Integration
Data Processing

Job Details

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Please find the job description below for a Java Fullstack Gen AI Developer and let me know your thoughts.

Role: Java Fullstack Gen AI Developer
Location: Remote
Visa: Any visa
Duration: Long term
Experience: 10+year

Job Description:
We are seeking a skilled Java Full Stack Developer with hands-on experience in Generative AI (GenAI) tools and Python development to join our fast-growing remote engineering team. You ll play a key role in building scalable web applications, integrating AI capabilities, and contributing to end-to-end software delivery from backend APIs to frontend experiences.

Key Responsibilities:
Design and develop robust backend services using Java, Spring Boot, and RESTful APIs.
Build responsive and dynamic user interfaces using React.js or Angular.
Integrate Generative AI models (LLMs, OpenAI, Hugging Face) into existing platforms.
Write and maintain data processing and ML-related modules in Python.
Collaborate with cross-functional teams including Data Scientists, DevOps, and Product Managers.
Implement secure, scalable cloud-native solutions (preferably on AWS, Azure, or Google Cloud Platform).
Maintain high standards of code quality through unit tests, code reviews, and CI/CD practices.

Required Skills & Experience:
5+ years of experience in Java Full Stack Development.
Proficiency in Spring Boot, Hibernate, Java 11+.
Strong frontend development using React, Angular, or Vue.js.
Solid experience with Python, especially in AI/ML-based projects.
Working knowledge of Generative AI frameworks (e.g., OpenAI API, LangChain, Hugging Face Transformers).
Understanding of LLM integration, prompt engineering, and vector databases.
Experience with REST APIs, Graph, and WebSocket communication.
Familiarity with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB).
Cloud experience with AWS, Azure, or Google Cloud Platform.
Version control using Git, and CI/CD pipeline experience (Jenkins, GitHub Actions, etc.).

Preferred Qualifications:
Experience deploying AI models into production.
Knowledge of Docker, Kubernetes, and microservices architecture.
Exposure to MLOps or DataOps practices.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

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