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Voice & Conversational AI Engineering

Build, deploy, and optimize production-grade real-time voice AI agents

10 phases. 55 lessons. 55 labs. 5 projects. The full voice AI stack: streaming audio architecture, speech-to-text (ASR), LLM orchestration for voice, text-to-speech (TTS), turn-taking detection, interruption handling, telephony integration, latency optimization, and production evaluation. You build real voice agents that answer phone calls, schedule appointments, qualify leads, and conduct natural spoken conversations — and graduate with a portfolio that proves you can ship voice AI in production.

10 phases · 55 lessons · 55 labs · 5 projects

Take ML & AI Engineering first — this course builds on it.

Outcomes you will have by the end

• 5 GitHub repos with working voice agents — Streaming ASR server, conversational voice agent, phone call agent, and production voice agent system — each with call recordings, evaluation reports, and architecture docs you can walk through in any voice AI interview.
• 1 deployed production voice agent — A live inbound customer support agent with CRM integration, monitoring dashboard, automated test suite, and fallback providers. A public endpoint and phone number you can demo to hiring managers.
• Verified Voice AI Engineer certificate — Issued by DeVenture Academy, tied to your completion record. Lists the specific voice AI skills, phases, and projects you completed.
• Voice AI Defense Practice Report — Weighted readiness score across audio streaming, ASR/TTS integration, turn-taking, telephony, latency optimization, and production deployment — exactly what hiring managers test for voice AI roles.
• Interview-ready voice AI case studies — Call recordings, latency profiles, conversation quality analyses, and system design docs for every project. Walk through them in any voice AI technical interview.
• 55 labs with real audio and real APIs — Every lesson has a lab where you stream real audio, call real APIs (Deepgram, ElevenLabs, OpenAI), measure real latency, and build real voice agents. No passive video watching — you develop the muscle memory that shows up in technical interviews.

What you will be able to do

Voice AI · ASR/TTS · WebRTC · Telephony · Turn-Taking · Latency Optimization

Every phase, every lesson, every project

• Voice AI Fundamentals & Architecture (5 lessons) — free — Voice agent architectures (chained, half-cascade, native audio), latency budgets, cost economics, audio fundamentals, why voice AI differs from text AI
  • What Voice AI Agents Are and How They Differ from Text AI and IVR
  • The Three Voice Agent Architectures
  • The Latency Budget and Cost Economics
  • Audio Fundamentals: Sample Rates, Codecs, and Formats
  • The Voice AI Landscape: Providers, Platforms, and Where the Field is Heading
• Audio Processing & Streaming (5 lessons) — free — Digital audio, PCM/μ-law/Opus formats, resampling, chunking with 20ms frames, buffering strategies, noise suppression, audio visualization, WebSocket audio streaming
  • Digital Audio Basics: Sample Rate, Bit Depth, and Channels
  • Audio Formats and Codecs: PCM, μ-law, Opus, G.711
  • Audio Resampling and Format Conversion
  • Audio Chunking, Buffering, and Noise Suppression
  • WebSocket Audio Streaming Server
• Speech-to-Text (ASR) (6 lessons) — ASR architectures (CTC, RNN-T, attention), streaming vs batch, Deepgram Nova-3, AssemblyAI Universal-2, Whisper, WER evaluation, custom vocabulary, accent handling, provider selection
  • How ASR Works: Acoustic Models, CTC vs RNN-T vs Attention
  • Streaming vs Batch ASR: Partial vs Final Transcripts
  • Deepgram Nova-3 Integration
  • AssemblyAI Universal-2 Integration
  • OpenAI Whisper and Self-Hosted ASR
  • ASR Evaluation: WER, TTFT, and Provider Selection
• Text-to-Speech (TTS) (5 lessons) — TTS architectures (autoregressive, diffusion, flow-matching), streaming TTS, ElevenLabs, Cartesia, PlayHT, OpenAI TTS, Hume AI, voice cloning, MOS evaluation, multilingual TTS
  • How TTS Works: Autoregressive, Diffusion, and Flow-Matching
  • Streaming TTS: Why First-Byte Latency Matters
  • ElevenLabs and Cartesia Integration
  • Voice Cloning and Custom Voices
  • TTS Evaluation: MOS, TTFB, and Provider Selection
• LLM Orchestration for Voice (5 lessons) — Model selection for voice, streaming LLM output, system prompt design for spoken conversation, context window management, tool/function calling during calls, RAG in voice agents, guardrails, cost optimization
  • Model Selection for Voice Agents
  • Streaming LLM Output and System Prompt Design for Voice
  • Context Window Management for Long Conversations
  • Tool/Function Calling During Voice Conversations
  • RAG, Guardrails, and Cost Optimization in Voice Agents
• Turn-Taking & Conversation Management (6 lessons) — VAD (Silero, WebRTC, semantic), endpointing (silence-based vs semantic), interruption handling (barge-in), backchanneling, conversation flow design, pacing and talk ratio, escalation to human
  • Voice Activity Detection (VAD): Silero, WebRTC, Semantic
  • Endpointing: Silence-Based vs Semantic
  • Interruption Handling (Barge-In)
  • Backchanneling
  • Conversation Flow Design and Pacing
  • Escalation to Human and Multi-Agent Handoffs
• Real-Time Transport & Telephony (6 lessons) — WebRTC architecture, WebSocket audio streaming, OpenAI Realtime API, Twilio Media Streams, SIP/PSTN fundamentals, phone number provisioning, STIR-SHAKEN, A2P 10DLC compliance, Twilio/Telnyx/Vonage comparison
  • WebRTC Architecture: SDP, ICE, DTLS, SRTP
  • WebSocket Audio Streaming: The OpenAI Realtime API Pattern
  • Twilio Media Streams: Streaming Bidirectional Audio on PSTN
  • SIP, PSTN, and Enterprise Phone System Integration
  • Telephony Compliance: STIR-SHAKEN, A2P 10DLC, TCPA
  • Telephony Provider Comparison and Selection
• Voice Agent Frameworks (6 lessons) — Pipecat (frame-based pipelines), LiveKit Agents (WebRTC rooms), OpenAI Realtime API (native speech-to-speech), Vapi (BYO stack), Retell AI (managed telephony), Bland AI (high-volume outbound), framework selection
  • Pipecat: Frame-Based Streaming Pipelines
  • LiveKit Agents: WebRTC-First Voice Agents
  • OpenAI Realtime API: Native Speech-to-Speech
  • Vapi: BYO-Stack Voice Agent Platform
  • Retell AI and Bland AI: Managed Telephony Platforms
  • Framework Selection: Choosing the Right Tool for the Job
• Evaluation, Testing & Production (6 lessons) — Latency metrics (TTFA, P50/P95/P99), ASR/TTS quality metrics, conversation quality (WPM, talk ratio, task completion), testing frameworks (Hamming, Cekura), post-call analytics, automated testing, production deployment, scaling, monitoring, fallback strategies, security
  • Latency Metrics: Glass-to-Glass, Per-Component, P50/P95/P99
  • Conversation Quality Metrics: WPM, Talk Ratio, Task Completion
  • Testing Frameworks: Hamming, Cekura, Coval
  • Post-Call Analytics: Transcription, Summarization, Sentiment
  • Production Deployment: Scaling, Fallback, Graceful Degradation
  • Monitoring, Alerting, and Security
• Advanced Topics & Capstone (5 lessons) — Native audio models (gpt-realtime-2, Nova Sonic, Moshi), multimodal voice agents, voice cloning, real-time translation, emotional AI (Hume EVI), voice biometrics, cost optimization at scale, career and portfolio
  • Native Audio Models: gpt-realtime-2, Nova Sonic, Moshi
  • Multimodal Voice Agents: Voice + Vision
  • Voice Cloning and Real-Time Translation
  • Emotional AI (Hume EVI) and Voice Biometrics
  • Cost Optimization at Scale and Career Preparation

The technologies you will use

Python · Pipecat · LiveKit · OpenAI Realtime API · Deepgram · ElevenLabs · Cartesia · Twilio · Vapi · WebRTC · FastAPI · AssemblyAI

Roles this course prepares you for

• Voice AI Engineer ($170k–$280k base, $250k–$420k TC) — Build end-to-end voice agent systems: streaming audio pipelines, ASR/TTS integration, LLM orchestration, turn-taking, telephony. The core role at Vapi, Retell, Bland, LiveKit, and AI startups.
• Speech AI Engineer ($128k–$222k base, $202k–$247k TC) — Specialize in the speech processing layer: ASR model selection, TTS optimization, acoustic modeling, streaming inference. Common at Deepgram, AssemblyAI, ElevenLabs, Cartesia.
• Conversational AI Engineer ($140k–$230k) — Design and build conversational logic: prompt engineering for voice, conversation flow design, multi-agent orchestration, guardrails. Focus on the dialogue management layer.
• Voice Agent Developer ($120k–$200k) — Build voice agents on managed platforms (Vapi, Retell, Bland) for specific business use cases. More product-focused, less infrastructure. Common at agencies and SMBs deploying voice agents.
• Real-Time Systems Engineer (Voice) ($160k–$280k) — Focus on the infrastructure layer: WebRTC, WebSocket, streaming audio, latency optimization, scaling. The plumbing expert who makes voice agents fast and reliable.
• AI Voice Product Engineer ($130k–$220k) — Combines voice AI engineering with product thinking: designing voice user experiences, conversation UX, voice agent personality, caller journey optimization. Common at consumer-facing companies.
• Voice AI Consultant / FDE ($150k–$250k) — Deploy voice agents for enterprise clients: scoping use cases, integrating with client telephony and CRM, compliance, production deployment. The FDE variant specialized in voice.
• Senior Voice/Speech ML Engineer ($200k–$480k TC) — Research-engineering hybrid: model fine-tuning, acoustic model optimization, TTS training, ASR evaluation. The highest-paying variant, common at frontier speech AI companies.

What Voice & Conversational AI Engineering actually is

Voice AI engineering is the discipline of building production systems that conduct real-time spoken conversations with users — answering phone calls, scheduling appointments, qualifying leads, handling customer support. It combines streaming audio architecture, speech-to-text, LLM orchestration, text-to-speech, turn-taking detection, telephony integration, and latency optimization into a single real-time pipeline. It is not "ChatGPT with a microphone" — it is the engineering layer that makes spoken AI conversations fast, natural, and reliable enough for production phone calls.

What you do every day

You build streaming audio pipelines that chain ASR, LLM, and TTS providers together with sub-500ms latency. You tune VAD thresholds to eliminate false interruptions. You integrate Twilio Media Streams so your agent can answer actual phone calls. You debug why calls cut off mid-sentence on noisy lines. You optimize the latency budget by parallel-streaming partial ASR transcripts into the LLM. You write system prompts designed for spoken conversation, not text. You measure P50/P95 latency on every call and tune until it feels natural.

Why companies are hiring for this now

Voice AI job postings surged 88% year-over-year in 2026, with 76% of companies unable to fill voice AI roles. AI voice agents cut per-call costs from $7–$12 (human) to ~$0.10–$0.20/minute — a 90–95% cost reduction driving massive enterprise adoption. The global voice AI market is projected to exceed $50B by 2030. Companies like OpenAI, ElevenLabs, Deepgram, Vapi, Retell AI, Bland AI, LiveKit, and Cartesia are hiring aggressively, and a voice agent engineer with shipped production WebRTC + Realtime API experience earns 30–50% more than a generalist ML engineer of the same seniority.

What this course is not

It is not a "use voice AI tools" course. You will not just call the OpenAI Realtime API. You will build the streaming audio pipeline, implement turn-taking from first principles, integrate telephony, navigate compliance, and optimize latency to production-grade levels. It is not a theory course — every lab involves real audio, real APIs, and real latency measurement. And it does not pretend voice AI is just text AI with audio bolted on: the real-time constraints, streaming architecture, turn-taking, and telephony layer make this a fundamentally different engineering discipline.

Common questions

What background do I need for the Voice & Conversational AI Engineering course?

Python proficiency and a basic understanding of LLM APIs (OpenAI, Anthropic). Familiarity with async programming (asyncio) and what APIs and websockets are. No prior audio engineering, telephony, or speech processing experience required — the course teaches the audio and telephony layer from scratch. We recommend the ML & AI Engineering course as a foundation, but it is not required.

Is this course standalone or does it require another course?

Fully standalone. If you already know Python and the basics of LLM APIs, you can start here directly. The first two phases (free) cover voice AI fundamentals and audio processing from first principles. If you're newer to AI engineering, completing the ML & AI Engineering course first will make the LLM orchestration phases easier.

How is this different from the ML & AI Engineering course?

The ML & AI Engineering course covers LLM engineering and agents as part of a broader AI engineering curriculum. This course goes deep into the layer no other course covers: real-time streaming audio, ASR/TTS integration, turn-taking and interruption handling, telephony integration (Twilio, SIP, PSTN), WebRTC transport, and latency optimization. These are completely different engineering challenges from text-based AI — they involve real-time constraints, audio processing, and telephony compliance that text AI never touches.

How is this different from the Agentic AI Engineering course?

The Agentic AI Engineering course teaches you how to build agents with tools, memory, and multi-agent coordination. This course focuses on voice-specific agent challenges: streaming audio instead of text, turn-taking and barge-in handling, telephony integration, and the latency budget that makes or breaks a voice agent. Phase 4 covers LLM orchestration specifically for voice — the rest of the course is about the audio, transport, and telephony layers that voice agents require.

How long does this course take?

130–170 hours of structured content. Most engineers complete it in 4–6 months at 8–10 hours per week. Phases 0–1 (free) can be completed in about two weeks and give you a real sense of the field before you commit further.

Do I need a phone number or special hardware?

You will need a Twilio trial account (free tier provides a trial phone number) for the telephony labs in Phase 6. For other phases, you need a microphone (built-in laptop mic is fine) and API keys for Deepgram, ElevenLabs, and OpenAI — all of which offer free-tier credits. No GPU or special audio hardware required.

What about API costs during the course?

Most labs use free-tier credits from Deepgram ($200 free), ElevenLabs (free tier), and OpenAI (free tier for new accounts). The telephony labs use Twilio trial credits. Total expected API spend during the course is under $30 if you use free tiers. The course teaches cost optimization as a core skill — you will learn to minimize per-minute costs as you build.

Will I build agents that actually make phone calls?

Yes. By Phase 6, you build a voice agent that answers actual phone calls via Twilio. By Phase 8, you deploy a production inbound customer support agent. The capstone projects produce working voice agents with call recordings you can demo in interviews — not simulations or mockups.

What specific jobs does this course prepare me for?

Voice AI Engineer, Speech AI Engineer, Conversational AI Engineer, Voice Agent Developer, Real-Time Systems Engineer (Voice), AI Voice Product Engineer, Voice AI Consultant/FDE, and Senior Voice/Speech ML Engineer. Companies hiring for these roles include OpenAI, ElevenLabs, Deepgram, Uber, Meta, Vapi, Retell AI, Bland AI, LiveKit, Cartesia, AssemblyAI, and hundreds of AI startups and agencies.

Do I need telephony experience?

No. Phase 6 teaches telephony from scratch: how phone calls work, SIP signaling, PSTN fundamentals, Twilio Media Streams, STIR-SHAKEN, and A2P 10DLC compliance. The course covers the unglamorous but essential telephony layer that every production voice agent deployment must navigate — including the 4-6 week compliance registration process that blocks most deployments.

Key terms in this course

Latency · Streaming · Agent · Orchestration · Chunking · Context Window · Function Calling · Guardrails

Continue your learning path

ML & AI Engineering · Agentic AI Engineering · Generative Media Engineering

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