Senior Backend Engineer - Speech Platform

About the Role

**Job Information** ------------------- * Date Opened 28/09/2026 * Job Type Full time * Industry Technology * Work Experience 4\-5 years * City Noida * Province Uttar Pradesh * Country India * Postal Code 201301 **Job Description** ------------------- **About role:** You'll be the engineering owner on the speech team, working alongside two ML engineers and building everything around the models — ingestion, training infrastructure, real\-time serving, and the integration into our existing platform. This is a backend engineering role. You don't need to train models. You need to build the systems that make trained models useful in production. **Responsibilities:** * Build the audio data pipeline: mine our call archive, transcode, resample, segment, deduplicate and quality\-filter at scale * Build and operate real\-time inference services with hard latency targets, including streaming, cancellation and mid\-utterance interruption * Integrate speech services into our existing Java\-based platform and telephony infrastructure * Instrument the full latency budget end to end and find where the milliseconds go * Stand up training infrastructure — GPU scheduling, checkpointing, experiment tracking, reproducibility * Own compute cost and concurrency economics: how many simultaneous calls per GPU, and how to improve it * Support on\-premise deployment for clients who can't send data outside their network ### **Requirements** **Must haves:** * 3–4 years building and operating production backend systems * Strong Java — you've owned services in production, not just contributed to them * Working Python — enough to build data pipelines and integrate with ML tooling * Real\-time or low\-latency systems experience: streaming APIs, WebSocket or gRPC streaming, concurrency, backpressure * Data pipelines at scale (Airflow, Dagster, Spark or equivalent) * Docker and Kubernetes in production * Cloud infrastructure (AWS/GCP/Azure) * Comfortable debugging performance: profiling, latency percentiles, throughput under load **Nice to have:** * Serving ML models in production (Triton, vLLM, TorchServe) * Audio tooling — ffmpeg, sox, codecs, resampling * Telephony — SIP, Asterisk/FreeSWITCH, media servers, narrowband codecs * MLOps tooling: MLflow, Weights \& Biases, DVC * GPU\-aware infrastructure work