Backend / ML-Ops Engineer — Speech Model Deployment & Inference Optimization at Outcomesai — IND | Base Career

Full Job Posting

Containerize and deploy speech models using Triton Inference Server with TensorRT/FP16 optimizations.
Develop and manage CI/CD pipelines for model promotion (staging → production).
Configure autoscaling on Kubernetes (GPU pools) based on active calls or streaming sessions.
Build health and observability dashboards : latency, token delay, WER drift, SNR/packet loss monitors.
Integrate LM bias APIs, failover logic, and model switchers for fallback to larger/cloud models.
Implement on-device or edge inference paths for low-latency scenarios.
Collaborate with AI team to expose APIs for context biasing, rescoring, and diagnostics.
Optimize GPU/CPU utilization, cost optimization, and memory footprint for concurrent ASR/TTS/Speech LLM workloads.
Maintain data and model versioning pipelines with MLflow, DVC, or internal registries .
Experience with Triton, TensorRT, Docker, Kubernetes , and GPU scheduling.
Familiarity with speech inference (streaming ASR, TTS pipelines).
Proficient in Python, Bash, and cloud services (AWS/GCP/Azure).
Understanding of observability stacks (Prometheus, Grafana, ELK).
Knowledge of DevSecOps , access policies, and PHI-safe environments.
Interest in inference optimization, mixed precision, and quantization.
B.Tech / M.Tech in Computer Science or related field.
4–6 years of experience in backend or ML-ops; at least 1–2 years with GPU inference pipelines.
Proven experience deploying models to production environments with measurable latency gains.

Backend / ML-Ops Engineer — Speech Model Deployment & Inference Optimization