Backend Engineer (Platform | Up to 45LPA)

This role is for one of our client companies — a VC-backed startup that has raised $7.52M USD in funding.\nSalary: Up to 45LPA\nApply once and, if selected, get access to up to 20 remote and onsite interview opportunities.\n\nWhat We’re Building\nCodeRound AI matches the top 5% of tech talent with fast-growing, VC-funded startups across Silicon Valley and India.\n\nTop-tier product startups across the US, UK, EU, UAE, and India have hired engineers through CodeRound.\n\nBackend Engineer (Platform)\nWe’re building a high-performance GenAI infrastructure platform that enables teams to deploy, scale, and monitor LLMs, speech, vision, and diffusion models across cloud and on-prem environments. Our focus is on low-latency inference, reliability, observability, and enterprise-grade security .\n\nWhat you’ll do\nBuild and scale backend services in Python (FastAPI / Django-style frameworks)\nDesign and own REST / gRPC APIs for model deployment, orchestration, and evaluation workflows\nDevelop production-grade systems with strong focus on testing, logging, and reliability\nWork hands-on with Docker & Kubernetes for deployments across environments\nBuild systems with an observability-first mindset (metrics, logs, tracing)\nDebug production issues and improve performance, scalability, and reliability\nContribute to system design and architecture decisions\n\nWhat we’re looking for\n5+ years of backend / platform engineering experience\nStrong Python fundamentals and backend system design experience\nGood understanding of:\nDistributed systems and HTTP-based architectures\nSQL databases (schema design, performance, migrations)\nCaching, queues, and background job processing\nAuthentication & authorization systems\nExperience working on production systems with ownership mindset\nStrong Git, CI/CD, and collaboration practices\nComfortable in fast-paced, high-ownership environments\n\nGood to have\nPostgres, Redis, Celery/RQ, Kafka or similar systems\nHands-on Kubernetes & Docker in production\nCloud exposure (AWS / GCP / Azure)\nExposure to ML systems or model inference workflows\nOpen-source contributions or strong engineering side projects\n\nWhy join\nWork on infrastructure that directly powers real-world AI workloads — where backend engineering meets cutting-edge GenAI systems at scale .