AI Voice Agent Optimization – Reduce Latency in Transcription-to-Notification Pipeline

Job Details

Description

Summary We have a working AI voice workflow that needs performance optimization, not a rebuild. The pipeline currently: records a voice note → transcribes it (STT) → generates a summary (LLM) → extracts action items (LLM) → sends notifications. All steps work correctly, but end-to-end response time is too slow and needs to feel near real-time. What we need: A review of the current architecture to pinpoint bottlenecks (sequential processing, model choice, queue/worker delays, notification delivery method, etc.) Concrete optimization recommendations with expected latency impact for each Implementation of the highest-impact fixes Before/after latency benchmarks so we can verify improvement Ideal Candidate: experience with async/streaming architectures, LLM pipeline optimization (parallelizing calls, prompt/model selection), and background job systems (Celery/Redis or similar). Please share a specific example of a latency problem you diagnosed and fixed, not just general experience.

Skills

Computer Vision Automatic Speech Recognition Python JavaScript Machine Learning API Java

Notification History

Channel	Type	Status	Sent	Error
telegram	pre_ai_job_alert	sent	Wed, Jul 22, 2026 3:34 PM	-

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