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Sr Full Stack Developer with AI
Tata Consultancy Services
Plano, USA
Full-time
Senior · 10+ years experience
Onsite
USD 120000-150000 yearly / year
Discovered 1 weeks ago
JavaSpring BootJ2EESOAPRESTXML
Free
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JavaSpring BootJ2EE
Key Skills for This Role
JavaSpring BootJ2EESOAPRESTXML
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Must Have Technical And Functional Skills
- 10+ years of hands-on experience in design and development using Java/SpringBoot Microservices
- Experience in Java, J2EE, SOAP, REST, XML, XSLT, UML, Object Oriented Analysis and Design
- Experience in Spring Boot, Eclipse, GIT, JENKINS, CI/CD, JIRA, JUnit testing
- Experience and exposure in RDBMS like Oracle, SQL Server, MySQL
- Experience and exposure in NoSQL Database like MongoDB, Document DB
- Have strong expertise and hands on experience in UI tech stack like ReactJS with JavaScript, Redux, HTML5 and CSS3 and writing cross-browser compatible code.
- Proficient with CSS-preprocessor e.g. SASS and hands on Experience in CSS frameworks e.g. Bootstrap
- Experience with integrating RESTful services
- Experience in developing responsive UI design to support multiple view port
- Thorough understanding of the responsibilities of the platform, database, API, caching layer, proxies
- Experience working with testing frameworks Jasmine, Karma etc.
- Experience in building scalable application
- Experience in performance tuning of UI application
- Experience in Search Engine Optimization and writing Search Engine Optimized code
- Coordinating with UX designer
- Cloud technology like AWS experience, Kubernetes
- Experience in JIRA and GITHUB.
- Experience in Apache Kafka framework and implementation
- Experience in MuleSoft integration and API platform
- Design and implement Gen AI applications using AWS Bedrock and related AWS services.
- Build secure, scalable, and maintainable AI workflows for enterprise use cases.
- Develop retrieval-augmented generation solutions that use enterprise knowledge sources effectively.
- Manage context windows, memory strategies, prompt composition, and grounding techniques.
- Integrate MCP-based tools and services to extend model capabilities and connect to enterprise systems.
- Collaborate with product owners, architects, data engineers, and security teams.
- Evaluate model outputs for quality, safety, relevance, and factual accuracy.
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