Core Java Software Engineer

2 days ago

Delhi, Delhi, India beBeeSoftware Full time ₹ 12,00,000 - ₹ 15,00,000

Job Overview:

We are seeking an experienced software professional to design, develop and optimize high-performance backend applications, services and APIs.

The ideal candidate will have expertise in Java, database architecture, data analytics and AI/ML integration. This role involves driving architectural decisions, mentoring team members and delivering scalable, data-driven solutions leveraging technologies like Kafka, Spark, Redis, ReactJS, PostgreSQL, MongoDB and Cloud Services (AWS/Azure/GCP).

  • Lead the development of robust Core Java applications (Standalone, Microservices and Batch Processing)
  • Architect and optimize scalable RESTful APIs for enterprise grade systems
  • Drive performance tuning, memory management, multi-threading and concurrency improvements
  • Collaborate with cross-functional teams, including frontend (ReactJS) and DevOps, for end-to-end delivery
  • Design, implement and optimize SQL/NoSQL databases for large-scale, high-volume datasets
  • Build and lead data analytics pipelines to extract actionable insights from structured and unstructured data
  • Integrate AI/ML models into production systems to enable intelligent automation and predictive capabilities
  • Ensure adherence to coding standards, performance benchmarks and security best practices
  • Mentor junior engineers and participate in technical reviews, architecture discussions and roadmap planning
  • Oversee cloud-native deployments and manage environments on AWS/Azure/GCP

Required Skills and Qualifications:

  • Expert-level Core Java (collections, streams, concurrency, JVM internals)
  • Proven experience with Spring Boot, Microservices and distributed system architectures
  • Advanced database skills - schema design, indexing, query optimization and performance tuning
  • Strong knowledge of event-driven architecture (Kafka), caching (Redis) and big data frameworks (Spark)
  • Proficiency in AI/ML model lifecycle - development, training, deployment and inference
  • Familiarity with data visualization and analytics tools (Apache Superset, Power BI, Tableau)
  • Understanding of cloud-native architectures (Docker, Kubernetes, serverless) and CI/CD pipelines

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