Senior Software Engineer

Summary: About the Role You'll be a core individual contributor on the Fault Management team, building near-real-time streaming pipelines and intelligent fault-processing capabilities within our AIOps platform. You own module delivery end-to-end — from low-level design through production support — and work with platform architects and PMs to ship high-impact features. Tech Stack Java · Spring Boot · Apache Kafka · Kafka Streams · Kafka Connect · Apache Spark Duties & Responsibilities: Job Description – Senior Software Engineer 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: