Senior Software Engineer

Summary:

Responsibilities

• Module Ownership
• Own one or more fault-management modules end-to-end
• Write LLD documents for every feature request and present for team review
• Drive technical decisions within your module — velocity vs. reliability trade-offs
• Near-Real-Time Pipelines
• Build event-driven pipelines with Kafka Streams and Kafka Connect for fault ingestion, correlation, and enrichment
• Implement Spark batch and micro-batch jobs for large-scale fault analytics
• Meet SLA targets for latency, throughput, and fault tolerance
• Backend Engineering
• Build cloud-native microservices using Java and Spring Boot
• Design idempotent consumers, DLQs, and back-pressure for exactly-once / at-least-once semantics
• Agentic Development
• Use LLM-assisted coding and AI pair-programming tools to accelerate delivery
• Integrate ML-powered anomaly detection and RCA into fault workflows
• Prototype new AI capabilities that improve automated fault resolution rates
• Debugging & Incident Support
• Diagnose issues across Kafka brokers, Streams topologies, Spark executors, and Spring Boot services
• Primary escalation point for critical production incidents in your module
• Write runbooks and post-mortems to prevent repeat incidents
• Quality & Operational Excellence
• Unit, integration, and contract tests; peer code reviews
• Observability — metrics, tracing, structured logging; on-call rotation
• Contribute to CI/CD and infra-as-code improvements

Required Skills

• 6+ years of hands-on backend / data engineering experience
• Production Kafka systems — topics, partitioning, consumer groups, offset management
• Kafka Streams DSL and Processor API; stateful and stateless topologies
• Kafka Connect — source and sink connectors, lifecycle management
• Apache Spark — Structured Streaming, DataFrames, Spark SQL at scale
• Java 11+ and Spring Boot 3.x — REST, security, reactive patterns
• Deep debugging across multi-threaded, distributed JVM systems
• LLD/HLD documentation; distributed systems principles (CAP theorem, eventual consistency)
• Good to Have
• AIOps or observability platform experience
• Agentic development workflows or LLM-integrated toolchains
• Telecom fault management standards (TM Forum, IETF YANG/NETCONF) or OSS/BSS domain knowledge
• Kubernetes, service meshes, cloud-native deployments (AWS / Azure / GCP)
• Pre-Requisites / Skills / Experience Requirements:

Qualifications

• 6+ years of hands-on backend / data engineering experience
• Production Kafka systems — topics, partitioning, consumer groups, offset management
• Kafka Streams DSL and Processor API; stateful and stateless topologies
• Kafka Connect — source and sink connectors, lifecycle management
• Apache Spark — Structured Streaming, DataFrames, Spark SQL at scale
• Java 11+ and Spring Boot 3.x — REST, security, reactive patterns
• Deep debugging across multi-threaded, distributed JVM systems
• LLD/HLD documentation; distributed systems principles (CAP theorem, eventual consistency)
• Good to Have
• AIOps or observability platform experience
• Agentic development workflows or LLM-integrated toolchains
• Telecom fault management standards (TM Forum, IETF YANG/NETCONF) or OSS/BSS domain knowledge
• Kubernetes, service meshes, cloud-native deployments (AWS / Azure / GCP)
• Pre-Requisites / Skills / Experience Requirements