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Senior Software Engineer – High Performance Developer
3 weeks ago
High-Performance Backend Developer Role
We are seeking a highly skilled software engineer to lead our team in designing, developing and optimizing high-performance backend applications, services and APIs. The ideal candidate will have expertise in Java, database architecture, data analytics and AI/ML integration.
The successful candidate will be responsible for 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).
Key Responsibilities:
- Design and develop 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
Preferred Skills:
- 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