Architect and Build scalable, high-performance backend services utilizing Java (Quarkus/Spring Boot) and Node.js architectures
Model and Evolve complex distributed domains using Domain Driven Design (DDD), bounded contexts, and event-driven patterns
Design and Implement robust asynchronous workflows and decoupled communications leveraging Kafka, AWS SQS, and AWS SNS
Own End-to-End Reliability by defining SLIs/SLOs, instrumenting observability via Datadog, and leading production incident response and log analysis
Optimize Data Architecture across relational (Aurora RDS), analytical (Google BigQuery), and caching layers (Redis)
Deploy and Operate cloud-native services on AWS/Azure utilizing Kubernetes (EKS) and driving CI/CD and progressive rollouts
Collaborate and Mentor across cross-functional Agile teams (Product, Mobile, QE, Data) to champion engineering excellence and continuous delivery
Requirements
5+ years of experience building, operating, and tuning production-grade backend services
Strong, production-proven mastery of Java (Quarkus/Spring Boot); additional backend proficiency in Node.js is highly valued
Deep understanding of pub/sub patterns, idempotency, eventual consistency, and sagas using Kafka, SQS, and SNS
Strong hands-on experience with schema design, query tuning, and migrations across Relational (PostgreSQL/MySQL), NoSQL, and Redis caching
Solid production experience deploying to AWS and/or Azure with containerized workloads running on Kubernetes (EKS)
Strong knowledge of Infrastructure as Code (Terraform/CloudFormation), GitOps, and rigorous automated testing (K6/Gatling/JMeter)
Solid grounding in RESTful API design (with gRPC/GraphQL/AsyncAPI exposure) and comprehensive APM/distributed tracing using Datadog
Hands-on experience maximizing productivity using cutting-edge tools like GitHub Copilot, Cursor, and Claude Code for software delivery
Experience structuring codebases for AI contribution by maintaining optimized READMEs, AGENTS.md/CLAUDE.md files, and reproducible local setups
Proven experience leveraging AI agents to drive end-to-end tasks, including code generation, complex refactoring, debugging, and automated PR workflows
Experience embedding AI into backend workflows while ensuring reliability, security, and reproducibility.