MODULE LEAD - Java

Key Responsibilities Design, develop, and maintain scalable backend applications using Java and Spring Boot. Develop RESTful and GraphQL APIs for enterprise-grade applications. Build and maintain microservices-based architectures with a focus on scalability, security, and performance. Implement event-driven solutions using Apache Kafka for asynchronous communication. Develop and optimize MongoDB database schemas, queries, and aggregation pipelines. Containerize applications using Docker and deploy services in cloud environments. Build, deploy, and maintain applications on AWS services such as EC2, ECS/EKS, Lambda, API Gateway, S3, RDS, CloudWatch, IAM, and VPC. Integrate AI-enabled applications using Model Context Protocol (MCP) where applicable. Collaborate with frontend developers, product owners, architects, and DevOps teams to deliver high-quality solutions. Write clean, maintainable, testable, and well-documented code. Participate in code reviews and mentor junior developers. Troubleshoot production issues and optimize application performance. Follow Agile/Scrum development methodologies and participate in sprint ceremonies. Required Skills 6+ years of experience in backend application development. Strong proficiency in Java (Java 11/17 or above). Extensive experience with Spring Boot and the Spring ecosystem. Solid understanding of Microservices Architecture. Experience with Apache Kafka for messaging and event streaming. Strong knowledge of Docker and containerized deployments. Hands-on experience with AWS cloud services. Experience designing and consuming GraphQL APIs. Strong experience with MongoDB and NoSQL database design. Good understanding of REST APIs and API security (OAuth2, JWT). Experience with Git and modern CI/CD pipelines. Strong understanding of object-oriented programming, design patterns, and SOLID principles. Familiarity with unit testing frameworks such as JUnit and Mockito. Preferred Skills Experience with Kubernetes (EKS/OpenShift) is a plus. Knowledge of Terraform or Infrastructure as Code. Experience with Redis or caching mechanisms. Exposure to Elasticsearch/OpenSearch. Familiarity with observability tools such as Prometheus, Grafana, ELK, or Splunk. Experience implementing AI integrations using MCP (Model Context Protocol), AI agents, or LLM-based applications. Knowledge of secure coding practices and application security.