Senior Backend Engineer (Java Spring Boot)

We're looking for a Senior Backend Engineer with strong Java and Spring Boot experience to build high-performance, production-grade backend systems You'll own backend design, build APIs/microservices, improve reliability and performance, and collaborate closely with product, frontend, data, and platform teams. Familiarity with Python and exposure to AI/ML systems are strong advantages.
Responsibilities:
Design, develop, and maintain scalable backend services using Java and Spring Boot.
Build REST/gRPC APIs and ensure clean contracts, versioning, and backward compatibility.
Work on microservices architecture, service orchestration, and distributed systems patterns.
Own performance, reliability, and observability (metrics, logs, tracing, alerts).
Improve latency, throughput, and cost through profiling, caching, and tuning.
Work with data stores (SQL/NoSQL) and handle schema design, indexing, and migrations.
Ensure strong engineering practices: code reviews, testing, CI/CD, and security.
Collaborate with Product/Design to translate requirements into robust technical solutions.
Mentor engineers and raise the bar on engineering quality.
Requirements:
6+ years of backend engineering experience (Senior Engineer level).
Strong hands-on experience in Java and Spring Boot (incl. Spring MVC, Spring Data, etc. ).
Experience designing and operating distributed systems/microservices in production.
Strong understanding of multithreading, concurrency, JVM fundamentals, and performance tuning.
Experience with SQL and NoSQL databases (e. g., Postgres/MySQL and MongoDB/Cassandra).
Familiarity with messaging/streaming (Kafka/RabbitMQ/PubSub).
Experience with Docker and Kubernetes and cloud-native deployment patterns.
Strong understanding of security (authn/authz, OWASP, secrets, and encryption basics).
Strong communication and problem-solving skills.
Product-based company experience (building and operating products at scale).
Good to Have:
Working knowledge of Python (scripting, services, automation, or data workflows).
Exposure to AI/ML or GenAI systems (e. g., integrating LLM APIs, embeddings, vector DBs, RAG pipelines, and model-serving interfaces).
Experience with Redis caching, rate limiting, circuit breakers, and idempotency patterns.
Experience with gRPC, event-driven architectures, or workflow engines (e. g., Temporal).
Familiarity with observability tooling (OpenTelemetry, Prometheus, Grafana, ELK, etc. ).

Experience
6-10 yrs