URGENT: LiveKit + Azure Voice AI Expert Needed: Latency Tuning & Prompt Optimization (3 hour task) -- 3

@ashinnd84

Hi there, we are a team of Python AI /ML, Full Stack Web and Mobile App Developers and we can do this project in no time. Thanks Ashish Kumar.

@novatech2210

Hello! I’ve been recommended by a Freelancer Recruiter who’s impressed with the potential of a Voice AI expert. I’ve just completed a similar project for another client with a clear pain point: optimizing latency and improving prompt efficiency. This job post is a perfect fit for my experience in building scalable voice AI infrastructure, with a focus on Dockerized solutions. We’re aiming to deliver a 50% reduction in manual latency and a 70% boost in prompt efficiency within 8 days, aligned with the 3-hour task. I’m confident this bid is $1125, which covers the budget and highlights our skills in Azure, AI, and real-time voice optimization. Chris | Lead Developer | Novatech Multiple 5-star reviews on real-time voice AI apps, OpenAI API integrations, and CRM-connected tools. Happy to discuss architecture and timeline. Let’s get started.

@RehabG126

Hi, I am a Frontend Developer with experience in Python, HTML, CSS, and JavaScript. I have completed a project for Microsoft Research on Upwork. I can deliver quality work on time. Let me know if you have any questions. Thanks!

@Anika1802

Hey! My approach has always been hands-on and meticulous. Understanding the central importance of your agents' interactions, I will not only be attending to configuring parameters but actually testing them to ensure minimal latency and maximum responsiveness. My familiarity with Azure OpenAI service and TTS engine positions me well to optimize latency using innovative techniques that maintain human-like experience at all times. Additionally, my proficiency in tool calling and logic fixing makes me adept at handling both the outbound bug where call dispositions are prematurely hung up, as well as implementing a graceful hangup trigger for inbound calls when customers end their conversations. Combining my technical capabilities with an adeptness in keeping communication lines clear, I can assure you timely delivery of your codebase and high-quality work that improves your customers' experience significantly. Let me optimize your Voice AI app for-success - to ensure better human-AI interactions & knowledgeable Hang-Up decision making

@ZohaibSathio

LiveKit voice agent stuck at 2.5-3s TTFT — I bring these down to human latency. I run a real-time STT→LLM→TTS engine at ~800ms end-to-end with barge-in, and a production voice platform handling 500K+ calls/month sub-800ms. For yours: tune the AgentSession (endpointing delay, allow_interruptions, preemptive generation), add Silero VAD + a semantic turn-detector to kill dead-air, and stream Azure OpenAI so latency is measured to first token, not full completion — targeting <700ms perceived. Plus a tightened system prompt for consistent behavior. Portfolio: https://www.freelancer.com/u/ZohaibSathio Quick question: are you on the LiveKit Agents framework (AgentSession) or a custom pipeline, and is Azure OpenAI streaming already enabled? — Zohaib, AI Engineer (Voice AI · Autonomous Agents · RAG)

@gourabcode

LIVEKIT AZURE — I've built and tuned production LiveKit agents on Azure STT/TTS + Azure OpenAI, so this scope is very familiar territory. For latency, I'd start by cutting TTFT with streaming token handling, tighter VAD endpointing/min-silence settings, and trimming any blocking calls in your session pipeline — that alone usually gets perceived latency well under a second when TTS is already fast like yours. On the prompt side, I'll rewrite the system prompts with hard brevity constraints (1-2 sentence caps, no filler) and restructure the tool descriptions so log_call_disposition only fires on explicit user confirmation, not assumption. For inbound, I'll add a clean hangup trigger tied to closing phrases like "goodbye" or "that's all," using a native LiveKit disconnect flow so it's graceful, not abrupt. I have my own LiveKit Cloud and Azure setup ready (Mumbai region, Azure OpenAI in Southeast Asia), so I can spin up your files, test real calls myself, and only hand back the codebase once latency and tool behavior are verified — with transcripts from LiveKit Cloud for both inbound and outbound as proof. Can start immediately and comfortably deliver within your 1-day window. Happy to hop on a quick call if you want to walk through the current setup first.

@Pralay0256q

Hello, I have experience building AI-powered applications using Python and integrating LLM APIs. I understand the importance of low-latency voice interactions and can optimize your Azure Voice AI workflow by analyzing each stage of the pipeline, reducing response times, improving prompt efficiency, and implementing streaming where appropriate. I am comfortable working with Azure OpenAI, Python, REST APIs, and prompt engineering. I write clean, maintainable code and can quickly understand your existing implementation to identify bottlenecks and improve overall performance. I can start immediately, provide regular progress updates, and deliver a well-tested solution within your timeline. I would be happy to discuss your current architecture and optimization goals before getting started. Thank you for your consideration. I look forward to working with you.

@groverhg

Hello, I hope you're having a great day. LIVEKIT AZURE I have hands-on work with LiveKit Python SDK AgentSession tuning and Azure OpenAI/TTS, having delivered several similar builds. I recently tuned a voice agent where TTFT dropped from 2.8s to ~600ms by adjusting VAD endpointing, room_options timeouts, and switching to Azure OpenAI streaming with chunked responses. I'd start by profiling AgentSession configs, tighten VAD thresholds, enable Azure OpenAI server-sent events, and reduce buffer sizes in the TTS pipeline (probably tweak native timeouts and VAD frames). Could we hop on a short call to confirm the expected hangup trigger wording and the exact log_call_disposition behavior before I modify prompts and tool descriptions? Thank you, Harshit G.

@UmairBuildsAI

Hey, I can help you optimize your existing LiveKit + Azure Voice AI stack without changing the working architecture. Since your SIP routing and infrastructure are already stable, I'll focus on reducing latency, tightening prompt behavior, and fixing the tool-calling and hangup logic. You can review my work here: https://www.freelancer.pk/u/UmairBuildsAI I have experience with AI voice agents, Python, Docker, Azure OpenAI, prompt engineering, and workflow automation. I'll tune the AgentSession/VAD settings, optimize Azure streaming for lower perceived TTFT, rewrite the system prompts for concise responses, and ensure the outbound agent never ends a call without explicit confirmation while implementing a graceful inbound hangup flow. Before delivery, I'll test the complete call flow, verify the latency improvements, and provide the updated code along with the required transcripts. Could you confirm the LiveKit and Python SDK versions you're currently using, and whether your Azure OpenAI deployment is already configured for streaming responses? Happy to jump on a quick call to review the project flow and get your project live smoothly. Best Regards, Umair