Senior Java Developer

Key Responsibilities

• Design, develop, and maintain enterprise-grade Java applications and microservices
• Architect and implement backend systems integrating AI/ML models into production applications
• Design and optimize complex PostgreSQL database schemas, queries, and stored procedures
• Collaborate with data science teams to operationalize ML models (model serving, inference pipelines, monitoring)
• Write clean, efficient, well-tested code following best practices (SOLID, design patterns, TDD)
• Participate in system design, code reviews, and architecture decisions
• Optimize application performance, scalability, and database query performance
• Troubleshoot production issues and lead root-cause analysis
• Mentor junior developers and contribute to technical standards
• Work closely with cross-functional teams (DevOps, QA, Product) in an Agile environment

Required Skills & Qualifications

• 8+ years of professional Java development experience (Java 8/11/17+)
• Strong experience with Spring Framework / Spring Boot, Hibernate/JPA
• Required: 3+ years of AI/ML experience including model integration, ML frameworks (TensorFlow, PyTorch, scikit-learn), or applying AI/ML concepts (NLP, predictive modeling, recommendation systems) within Java-based applications
• Required: 3+ years of PostgreSQL experience including schema design, query optimization, indexing, stored procedures, and performance tuning
• Strong understanding of RESTful API design and microservices architecture
• Experience with message queues (Kafka, RabbitMQ) and caching (Redis)
• Solid understanding of data structures, algorithms, and object-oriented design
• Experience with unit testing frameworks (JUnit, Mockito)
• Familiarity with version control (Git) and CI/CD pipelines
• Excellent problem-solving, communication, and leadership skills

Preferred Qualifications

• Experience deploying ML models via APIs (Flask/Fast API) consumed by Java services
• Cloud platform experience (AWS, Azure, or Google Cloud Platform)
• Containerization experience (Docker, Kubernetes)
• Experience with big data tools (Spark, Hadoop)
• Exposure to MLOps practices and model lifecycle management