About the role
About Sarvam Sarvam is building the bedrock of Sovereign AI for India. The company is developing India’s full-stack sovereign AI platform, building across research, models, infrastructure and applications with a singular focus on making AI genuinely work for India. Sarvam works with leading enterprises and public institutions and is backed by Lightspeed, Peak XV, and Khosla Ventures. Sarvam partners with India’s leading brands, including Tata Capital, SBI Life, CRED, IDFC, and LIC.
About
the Role We are hiring a Backend Engineer to work across Sarvam’s Studio media platform — spanning AI dubbing, live translation, and the shared service foundation that powers all Studio products (voice cloning, stem separation, lip sync, music generation, and more). You will build and maintain production services, ML pipeline libraries, and platform SDKs that together enable multilingual media processing at scale for enterprise customers and Sarvam Studio users. The work cuts across multiple codebases: a core ML pipeline library (ASR, translation, TTS, audio processing), production services for dubbing and live translation, and a shared platform SDK that provides common capabilities to every Studio service. What You’ll Do Service & Infrastructure Design and optimize production FastAPI services for dubbing and live translation — multi-stage task orchestration, rate-limited scheduling, and backpressure controls for concurrent workloads Build and maintain distributed worker architectures with independent scaling per pipeline stage and automatic recovery of stuck or failed tasks Own the data layer — async ORM models, schema migrations, and query optimization on PostgreSQL Implement real-time features — WebSocket-based job tracking for dubbing and streaming audio pipelines for live translation Manage Kubernetes deployments — Helm charts, secrets management, ingress configuration, and multi-role container images ML Pipeline & Library Extend and maintain the core dubbing library across all pipeline stages: audio extraction, VAD, speech recognition, translation, QC, TTS, and final video stitching Integrate and optimize ML model serving — remote inference server clients and local model inference for audio analysis and vocal separation Build and improve QC orchestration — automated scoring, tempo analysis, guided normalization, and pronunciation verification Design async-first pipelines with efficient concurrency patterns for CPU-bound audio processing Maintain and evolve LLM integration layers for translation, QC, and pre-processing across multiple provider backends Platform SDK & Shared Services Build and maintain the shared Studio service SDK — reusable FastAPI middleware and routers for authentication, billing, workspace isolation, and input validation Design media storage abstractions — upload, signed URL generation, retention policies, and cloud blob storage integration Implement cross-cutting concerns: rate limiting, metering, audit trails, and request history across all Studio services Build observability foundations — OpenTelemetry instrumentation, structured logging, and metrics collection shared across services Quality & Operations Maintain high test coverage with strong CI gates, parallel test execution, and thorough mocking of external services nstrument services with custom metrics and structured error tracking for production observability Manage CI/CD pipelines — automated testing, linting, container builds, artifact publishing, and version management What We’re Looking For 4–6 years of experience in backend engineering, with a focus on building and operating production services at scale Strong proficiency in Python with hands-on experience building production FastAPI or similar async web services (non-negotiable) Deep understanding of async programming — asyncio, concurrent execution patterns, and designing for high-throughput workloads Experience with distributed task systems : task queues (Celery or similar), message brokers, and designing fault-tolerant job orchestration Hands-on with PostgreSQL and an async ORM (SQLAlchemy preferred) — comfortable with query optimization, schema design, and migrations Familiarity with audio/media processing : FFmpeg, common audio formats, and processing libraries like soundfile or librosa Experience integrating ML models into production — API-based (REST/gRPC) or local inference (PyTorch, ONNX Runtime) Experience building reusable libraries or SDKs — designing clean APIs, managing backward compatibility, and publishing packages for internal consumers Proficiency with Docker, Kubernetes , Helm charts, and at least one major cloud platform (Azure/GCP/AWS) Strong testing discipline — writing thorough tests, mocking external services, and maintaining CI/CD pipelines
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