Job Summary :

We are seeking a highly skilled and motivated Java Spring Boot Developer to join our engineering team. This role focuses on developing and deploying scalable, event-driven applications on OpenShift, with data ingestion from Apache Kafka and transformation logic written in Apache Camel. The ideal candidate should possess a strong understanding of enterprise integration patterns, stream processing, and protocols, and have experience with observability tools and concepts in AI-enhanced applications.

Key Responsibilities :

- 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.