Sr. Java Developer With GenAI

Job Title: Sr. Java Developer With GenAI

Location: New York NY

Project Duration: 12 months

Work Mode: Initially Remote (but later it will onsite)

Interview Mode: 1 Round with LTM 3 client round (one in-person meet will be required)

Job Descriptions:

Responsibilities
Experience in Java framework development
Experience building and managing a highly technical team working on business critical applications
Hands on Able and willing to write well documented high performance reliable maintainable code
Excellent design and programming experience in Core JavaJEE Concurrency REST APIs Spring Boot Framework and microservices GraphQL is a plus
Full working knowledge of software engineering principles design patterns refactoring and product development practices in complex enterprise cloud environments
Excellent core object-oriented and functional programming skills
Excellent working knowledge of Git source control management
Working knowledge of SQL and NoSQL databases
Experience with one or more APM frameworks with a solid understanding of integration with OAuth2 OIDC and JWT
Good understanding of containerization infrastructure virtualization technologies such as Docker and Kubernetes
Solid experience with C4 software architecture models or DFDs
Metrics driven development experience with an emphasis on observability
Working knowledge of 12Factor cloud native apps and CICD
Proficient with AWS and/or GCP
Strong analytical and problem-solving skills with attention to detail
Hardworking with a whatever it takes attitude Someone that gets things done
Able to demonstrate effective teamwork both within the immediate team and across functional teams
Excellent communications skills and able to lead a team of highly technical individuals
Familiar with Agile SCRUM SAFe development methodologies
Excellent organizational and planning skills

Gen AI
Hands on experience to rewrite enrich and maintain manual test cases coverage clarity reusability using Claude or relevant data model
Use GenAI to generate functional test cases from user stories requirements and expand edge negative scenarios
Drive manual QA enrichment by adopting AI tools for test design scenario generation and test data suggestions
Ensure AI outputs are grounded using project context requirements past defects known issues via RAG concepts
Familiarity with Agentic AI frameworks task planning tool usage iterative refinement to improve test workflows
Basic familiarity with Docker to run validate AI utilities or test tooling in a containerized setup