Senior Backend Engineer - Speech Platform

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