OutcomesAI

Full-stack Engineer

• Lead the end-to-end technical development of speech models (ASR, TTS, Speech-LLM) — from architecture, training strategy, and evaluation to production deployment.
• Act as an individual contributor and mentor, guiding a small team working on model training, synthetic data generation, active learning, and inference optimization for healthcare applications.
• Own the technical roadmap for STT/TTS/Speech LLM model training.

Tech Lead – ASR, TTS, Speech LLM, IC, Mentor

• Lead the end-to-end technical development of speech models (ASR, TTS, Speech-LLM) — from architecture, training strategy, and evaluation to production deployment.
• Act as an individual contributor and mentor, guiding a small team working on model training, synthetic data generation, active learning, and inference optimization for healthcare applications.
• Own the technical roadmap for STT/TTS/Speech LLM model training: from model selection → fine-tuning → deployment.
• Evaluate and benchmark open-source models (Parakeet, Whisper, etc.) using internal test sets for WER, latency, and entity accuracy.
• Design and review data pipelines for synthetic and real data generation (text selection, speaker selection, voice synthesis, noise/distortion augmentation).
• Architect and optimize training recipes (LoRA/adapters, RNN-T, multi-objective CTC + MWER).
• Lead integration with Triton Inference Server (TensorRT/FP16) and ensure K8s autoscaling for 1000+ concurrent streams.
• Implement Language Model biasing APIs, WFST grammars, and context biasing for domain accuracy.
• Guide evaluation cycles, drift monitoring, and model switcher/failover strategies.
• Mentor engineers on data curation, fine-tuning, and model serving best practices.
• Collaborate with backend/ML-ops for production readiness, observability, and health metrics.