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:
