- Lead, mentor, and inspire a high-performing engineering team delivering enterprise-grade products.
- Drive architecture, design, development, testing, deployment, and maintenance of scalable business applications.
- Design and implement low-latency, highly available, and high-performance RESTful APIs using Java and Spring Boot.
- Ensure development of reusable, maintainable, secure, and testable code following industry’s best practices.
- Conduct architecture reviews, design discussions, code reviews, and technical mentoring sessions.
- Own technical documentation including HLDs, LLDs, API specifications, and solution design documents.
- Troubleshoot complex production issues and drive root cause analysis and permanent fixes.
- Collaborate with Product Management, QA, DevOps, Product Engineering, and other cross-functional teams to deliver high-quality product releases.
- Leverage AI-powered software engineering tools (such as GitHub Copilot, Claude, ChatGPT, Cursor or equivalent) to accelerate software development across the SDLC.
- Utilize AI for requirement analysis, solution design, code generation, unit test generation, documentation, debugging, refactoring, and code optimization.
- Identify opportunities to automate repetitive engineering activities using AI-assisted development practices.
- Establish AI-assisted coding standards while ensuring security, maintainability, and code quality.
- Mentor team members on effective adoption of AI engineering tools and best practices.
- Evaluate AI-generated code for correctness, scalability, security, and compliance with engineering standards.
- Drive continuous productivity improvements through responsible AI adoption.
Key Responsibilities
Technical Leadership
- Lead, mentor, and inspire a high-performing engineering team delivering enterprise-grade products.
- Drive architecture, design, development, testing, deployment, and maintenance of scalable business applications.
- Design and implement low-latency, highly available, and high-performance RESTful APIs using Java and Spring Boot.
- Ensure development of reusable, maintainable, secure, and testable code following industry’s best practices.
- Conduct architecture reviews, design discussions, code reviews, and technical mentoring sessions.
- Own technical documentation including HLDs, LLDs, API specifications, and solution design documents.
- Troubleshoot complex production issues and drive root cause analysis and permanent fixes.
- Collaborate with Product Management, QA, DevOps, Product Engineering, and other cross-functional teams to deliver high-quality product releases.
- Leverage AI-powered software engineering tools (such as GitHub Copilot, Claude, ChatGPT, Cursor or equivalent) to accelerate software development across the SDLC.
- Utilize AI for requirement analysis, solution design, code generation, unit test generation, documentation, debugging, refactoring, and code optimization.
- Identify opportunities to automate repetitive engineering activities using AI-assisted development practices.Establish AI-assisted coding standards while ensuring security, maintainability, and code quality.
- Mentor team members on effective adoption of AI engineering tools and best practices.
- Evaluate AI-generated code for correctness, scalability, security, and compliance with engineering standards.
- Drive continuous productivity improvements through responsible AI adoption.
AI-Driven Engineering Responsibilities
- Leverage AI-powered software engineering tools (such as GitHub Copilot, Claude, ChatGPT, Cursor or equivalent) to accelerate software development across the SDLC.
- Utilize AI for requirement analysis, solution design, code generation, unit test generation, documentation, debugging, refactoring, and code optimization.
- Identify opportunities to automate repetitive engineering activities using AI-assisted development practices.
- Establish AI-assisted coding standards while ensuring security, maintainability, and code quality.
- Mentor team members on effective adoption of AI engineering tools and best practices.
- Evaluate AI-generated code for correctness, scalability, security, and compliance with engineering standards.
- Drive continuous productivity improvements through responsible AI adoption.
Expected Business Outcomes
The successful candidate is expected to contribute towards measurable engineering improvements including:
- 20–30% improvement in engineering productivity through effective AI adoption.
- Reduction in development effort across SDLC phases including design, coding, testing, and documentation.
- Faster feature delivery and reduced release cycle time.
- Lower engineering costs through improved developer efficiency.
- Improved code quality through AI-assisted reviews and automated testing.
- Higher unit test coverage and reduced production defects.
- Increased automation across development and deployment processes.
Technical Skills
Mandatory
- Strong proficiency in Java (Java 11/17+)
- Spring Boot
- Spring Framework
- Microservices Architecture
- REST API Development
- Kafka
- RDBMS (Oracle/PostgreSQL/MySQL)
- Multithreading & Concurrency
- Design Patterns
- Distributed Systems
- API Security (OAuth2/JWT)
- Unit Testing (JUnit/Mockito)
- CI/CD (Jenkins/GitLab)
- SonarQube
- Git
Good to Have
- Kubernetes
- Docker
- Cloud Platforms (AWS/Azure/GCP)
- Redis
- Elasticsearch
- Prometheus/Grafana
AI & Developer Productivity Skills
Candidates should demonstrate hands‑on experience using AI‑assisted software engineering tools for improving engineering efficiency.
Preferred Exposure Includes
- GitHub Copilot
- Claude
- ChatGPT
- Cursor AI
- Amazon Q Developer
- IntelliJ AI Assistant
- AI-powered code review tools
- AI-assisted documentation generation
- AI-assisted unit test generation
- AI-assisted debugging and performance optimization
The Candidate Should Understand
- Prompt engineering for software development
- Responsible AI usage
- AI-assisted code validation
- Secure AI-assisted development practices
- AI-driven SDLC optimization
Leadership Skills
- Proven experience leading engineering teams of 5–6 developers.
- Strong analytical and technical implementation decision‑making capabilities.
- Experience driving engineering best practices and continuous improvement initiatives.
Engineering Excellence Expectations
- Maintain high code quality standards.
- Improve code coverage through automated testing.
- Drive technical debt reduction initiatives.
- Promote DevSecOps and CI/CD best practices.
- Ensure adherence to secure coding guidelines.
- Champion AI-enabled engineering transformation across the team.
Preferred Experience
- 6–9 years of enterprise software product development experience.
- Experience working in Agile/Scrum environments.
- Exposure to Telecom, FinTech, Digital Payments, or large‑scale enterprise products is preferred.
- Experience leading end-to-end product development from design through production support.