Software Development Engineer 2

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)