Java Full Stack Developer – AI & AWS

Job Description: Java Full Stack Developer – AI & AWS

Work Location

Fort Worth, TX 76109 / Phoenix, AZ / Charlotte, NC / Detroit, MI

Duration

6 Months

Experience Required

8–10 Years

Education

Bachelor’s or Master’s Degree

Interview Process

In-person interview required

Job Summary

We are looking for an experienced Java Full Stack Developer with strong expertise in Java, Spring Boot, Microservices, ReactJS, AWS, and Python. The ideal candidate should have hands-on experience developing scalable web applications and integrating AI/LLM technologies, including RAG, embeddings, and vector search.

Must-Have Skills

• 8–10 years of overall experience, including at least 5 years in Java Full Stack Development.
• Strong experience with Java, Spring Boot, Spring Cloud, REST APIs, and Microservices.
• Hands-on experience with ReactJS, TypeScript/JavaScript, HTML5, and CSS3.
• Experience with AWS, Hibernate/JPA, SQL/NoSQL databases, and Redis caching.
• Working knowledge of Python for automation and AI integration.
• Practical knowledge of LLMs, prompt engineering, embeddings, vector search, RAG, and responsible AI.
• Experience with unit testing and automation tools such as JUnit, Mockito, REST Assured, Jest, or Cypress.
• Experience with Git and Agile development methodologies.

Good-to-Have Skills

• Docker, Kubernetes, or OpenShift.
• Amazon Bedrock, Azure OpenAI, or similar AI platforms.
• AI agents, tool calling, model evaluation, and AI observability.
• OAuth, JWT, Spring Security, SonarQube, and DevSecOps practices.
• Experience in the Airlines domain is preferred. Airlines and Finance domain experience will be an added advantage.

Key Responsibilities

• Develop end-to-end web applications using Java, Spring Boot, ReactJS, and TypeScript.
• Build scalable REST APIs, microservices, and cloud-native applications on AWS.
• Design and optimize data-access layers using Hibernate/JPA, SQL/NoSQL databases, and Redis.
• Integrate LLM APIs, RAG workflows, semantic search, and AI-powered assistants.
• Implement prompt engineering, grounded responses, structured outputs, and responsible AI controls.
• Develop automated tests and support CI/CD, application monitoring, security, and performance optimization.
• Collaborate with Product, UX, DevOps, QA, and AI teams in an Agile environment.