This is an in-person office role in Hyderabad. About Role Were hiring a Senior Backend Engineer to design and scale services that power video, audio, and text based inputs to our multimodal LLM systems. Youll build high-performance APIs, orchestration layers, and async pipelines that can handle hundreds of thousands of concurrent users , spiky upload traffic, and compute-intensive inference workflows. Youll work closely with DevOps, ML, and frontend teams to build horizontally scalable , observability-rich , and fault-tolerant backend services that run in a cloud-native, Kubernetes-based environment. This is a foundational role with direct impact on system performance, developer velocity, and the quality of AI-driven experiences. Key Responsibilities Build scalable services in Python (FastAPI preferred) with a focus on stateless APIs , idempotent operations , and graceful failure modes . Design systems with horizontal scaling , partition tolerance , and data consistency trade-offs in mind. Build asynchronous processing pipelines for video ingestion, media preprocessing (e.g., encoding, chunking), and LLM inference triggering. Architect and operate messaging infrastructure (Kafka, RabbitMQ, Redis Streams, AWS SQS, Celery) for decoupled and resilient task execution. Optimize backend infrastructure using Kubernetes (HPA/VPA, Cluster Autoscaler), sharded databases , and replica-aware routing . Design, build, and maintain database schemas for high-throughput data tracking (uploads, job states, LLM output), optimizing for large-scale workloads. Contribute to architectural decisions involving service mesh , API gateways , and observability pipelines . Work with engineers across the stack including Node.js/React services to ensure seamless integration and data flow across the system. Maintain high-quality CI/CD pipelines and deployment workflows, with support for canary/blue-green strategies and rollback safety. Must-Have Skills Expert-level Python backend development for high-throughput, low-latency systems (FastAPI, asyncio, uvicorn/gunicorn stack). Strong experience with message queues and async task execution using Kafka, RabbitMQ, Redis Streams, SQS, or Celery. Deep familiarity with Kubernetes orchestration: autoscaling, rollout strategies, pod/network tuning, and node pool management. Strong command over relational (PostgreSQL) and NoSQL (MongoDB, Redis) databases: modeling, indexing, tuning, partitioning. Knowledge of distributed caching , rate limiting , circuit breakers , and backpressure strategies. Familiarity with chunked/resumable uploads , file session tracking, and large-object handling. Exposure to CQRS , event sourcing , message-driven design , and domain-driven architecture . Experience with observability stacks : Prometheus, Loki, Grafana, Tempo, and OpenTelemetry for distributed tracing, logging, and metrics.