Summary:
We're looking for a backend-strong individual contributor with 3–8 years of experience to join the Fault Management team within our AIOps platform. You'll own modules end-to-end — designing LLDs, building near-real-time streaming pipelines using Kafka Streams, Kafka Connect, and Apache Spark, and developing microservices in Java and Spring Boot. You'll be expected to debug complex distributed systems, act as the primary escalation point for critical production issues, and actively leverage agentic/LLM-assisted development tools. Strong knowledge of Kafka internals and distributed systems principles is a must. Experience in AIOps, telecom OSS/BSS, or cloud-native deployments is a plus.Duties & Responsibilities:
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
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
- 3–8 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)
Pre-Requisites / Skills / Experience Requirements:
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)
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