Senior Technical Lead - Spring Boot, React.js, Java

Hyderabad, Telangana

Job Summary

Builds LLM-integrated features on the Chubb AI Factory engagement, combining a Java Spring Boot backend with modern JS/TypeScript front-end work and direct Claude/Anthropic API integration.

Key Responsibilities

Build full stack features using Spring Boot backends and modern JS/TypeScript front ends

Integrate LLM APIs – streaming, retries, token budgeting, error classification

Apply prompt engineering practices; diagnose degradation at edge cases

Contribute to RAG pipeline integration – consuming embeddings and vector-store results

Write clean, observable REST/async APIs in Spring Boot

Use Claude Code/Copilot as a force multiplier with critical review before merging

Must-Have Skills

6–8 yrs full stack development with deep Java (Spring Boot) expertise

Working fluency in TypeScript/JavaScript and a modern front-end framework

Solid OOP foundation – DI, JPA/Hibernate, service layer patterns

Non-trivial SQL and JSON/XML handling

Azure basics – containerised deployment, CI/CD, log analysis

Good to Have

Hands-on LLM API integration experience

Vector DB / RAG pipeline literacy

Non-Technical Competencies

AI-native mindset – understands LLM limits and failure modes

Ambiguity tolerance – delivers against incomplete or evolving requirements

Communication clarity – explains technical decisions to both engineers and business stakeholders

Responsible AI awareness – flags PII/PHI exposure and knows when output needs human review

Key Responsibilities

1. Lead and mentor a team of developers in designing and implementing high-quality, scalable, and secure applications using spring boot, react..js, and java.

2. Collaborate with stakeholders to understand requirements, provide technical guidance, and ensure timely delivery of solutions.

3. Drive best practices in coding, design, and development methodologies within the team.

4. Conduct code reviews, performance tuning, and troubleshooting to ensure optimal performance of applications.

5. Stay updated with the latest technologies and industry trends to suggest improvements and innovative solutions.

Skill Requirements

1. Proficiency in spring boot framework for developing and deploying enterprise level applications.

2. Strong experience in developing frontend applications using react..js, including state management and component architecture.

3. Expertise in java programming language for backend development and integration with databases.

4. Solid understanding of restful services, microservices architecture, and cloud technologies.

5. Ability to lead a technical team, prioritize tasks, and communicate effectively with stakeholders.

Other Requirements

Builds LLM-integrated features on the Chubb AI Factory engagement, combining a Java Spring Boot backend with modern JS/TypeScript front-end work and direct Claude/Anthropic API integration.

Key Responsibilities

Build full stack features using Spring Boot backends and modern JS/TypeScript front ends

Integrate LLM APIs – streaming, retries, token budgeting, error classification

Apply prompt engineering practices; diagnose degradation at edge cases

Contribute to RAG pipeline integration – consuming embeddings and vector-store results

Write clean, observable REST/async APIs in Spring Boot

Use Claude Code/Copilot as a force multiplier with critical review before merging

Must-Have Skills

6–8 yrs full stack development with deep Java (Spring Boot) expertise

Working fluency in TypeScript/JavaScript and a modern front-end framework

Solid OOP foundation – DI, JPA/Hibernate, service layer patterns

Non-trivial SQL and JSON/XML handling

Azure basics – containerised deployment, CI/CD, log analysis

Good to Have

Hands-on LLM API integration experience

Vector DB / RAG pipeline literacy

Non-Technical Competencies

AI-native mindset – understands LLM limits and failure modes

Ambiguity tolerance – delivers against incomplete or evolving requirements

Communication clarity – explains technical decisions to both engineers and business stakeholders

Responsible AI awareness – flags PII/PHI exposure and knows when output needs human review

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