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Java Developer

5 - 9 years

6 - 15 Lacs

Posted:7 hours ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

Job Title: Java Spring boot Developer

Location:

Job Summary :

Java Spring Boot Developer

  • Key Responsibility :

    • Design, develop, and deploy

      Java Spring Boot

      applications on

      Red Hat OpenShift

      .
  • Build robust data pipelines with

    Apache Kafka

    for high-throughput ingestion and real-time processing.
  • Implement transformation and routing logic using

    Apache Camel

    and

    Enterprise Integration Patterns (EIPs)

    .
  • Develop components that interface with various protocols including

    HTTP

    ,

    JMS

    , and database systems (SQL/NoSQL).
  • Utilize

    Apache Flink

    or similar tools for complex event and stream processing where necessary.
  • Integrate observability solutions (e.g., Prometheus, Grafana, ELK, OpenTelemetry) to ensure monitoring, logging, and alerting.
  • Collaborate with AI/ML teams to integrate or enable AI-driven capabilities within applications.
  • Write unit and integration tests, participate in code reviews, and support CI/CD practices.
  • Troubleshoot and optimize application performance and data flows in production environments

Required Skills & Qualification

  • 5+ years

    of hands-on experience in Java development with strong proficiency in

    Spring Boot

    .
  • Solid experience with

    Apache Kafka

    (consumer/producer patterns, schema registry, Kafka Streams is a plus).
  • Proficient in

    Apache Camel

    and understanding of

    EIPs

    (routing, transformation, aggregation, etc.).
  • Strong grasp of various

    protocols

    (HTTP, JMS, TCP) and

    messaging paradigms

    .
  • In-depth understanding of

    database concepts

    both relational and NoSQL.
  • Experience with

    stream processing technologies

    such as

    Apache Flink

    , Kafka Streams, or Spark Streaming.
  • Familiarity with

    OpenShift

    or similar container platforms (Kubernetes, Docker).
  • Knowledge of

    observability tools

    and techniques – logging, metrics, tracing.
  • Exposure to

    AI concepts

    (basic understanding of ML model integration, AI-driven decisions, etc.).
  • Troubleshoot and optimize application performance and data flows in production environments

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