How to Become a
Voice AI Engineer
Specialize in the fastest-growing AI interface.
Voice AI Engineers build speech-to-text, text-to-speech, and conversational voice agents—earning $130K-$200K+.
AI Native Engineer Community Access
Want to Specialize in Voice
and Conversational AI?
Voice interfaces are exploding—from AI receptionists to enterprise call automation. You want to specialize but don't know where to start.
Voice AI requires unique skills: real-time processing, speech recognition, latency optimization. General LLM knowledge isn't enough.
Companies are building voice agents for customer service, healthcare, and sales. Specialists command premium salaries.
The Voice AI Engineering Path
The AI Career Accelerator
Voice AI Engineers combine LLM skills with speech processing and real-time systems. Here's how to build this high-demand specialization.
Master LLM Fundamentals
Build foundation in prompt engineering, RAG, and conversational AI
Learn Speech Processing
Understand STT (Whisper), TTS (ElevenLabs), and audio pipelines
Build Real-Time Systems
Master WebSockets, streaming, and low-latency architecture
Specialize in Voice Agents
Build end-to-end voice agents for customer service and automation
Meet Your Mentor
When I started in tech, I was based in the Netherlands with no connections and only thousands of video game hours under my belt. Not exactly the ideal starting point.
My first tech job was software tester. One of the most junior roles you can start with. I was just happy someone took a chance on me.
I kept learning. Kept pivoting. But what actually accelerated my career wasn't more certifications or more code. It was learning to solve problems that matter and proving beyond a doubt that what I built solved real problems. That's the skill that stays future-proof, even with AI.
I've since worked remotely for international software companies throughout my career. Proof that the high-paid remote path is possible for anyone with the right skills and motivation. In the end, I went from a $500/month internship to 6 figures as a Senior AI Engineer at GitHub.
Now I teach over 22,000 engineers on YouTube. Becoming an AI-Native Engineer is a system I lived through and offer to you today.
Real Results
Vittor
AI Engineer
Landed his first AI Engineering role in 3 months
"The coaching played a huge part in my success. I focused on AI fundamentals, the certification path, and soft skills like professional writing. Having access to expert guidance gave me confidence during interviews and helped me feel I was on the right path.
I built my own platform (simple but functional) and deployed it on AWS. I used it in my portfolio and showcased it during interviews. The way complex topics were explained, especially the restaurant analogy for AI systems, really stuck with me. Focusing on doing the basics well was absolutely essential."
What You Will Get
Personalized Roadmap & Career Strategy
A custom plan tailored to your background, goals, and timeline. No generic advice.
Weekly 1:1 Coaching Calls
Direct access to Zen for guidance, project feedback, and answers to your questions.
Portfolio-Ready AI Projects
Build production-grade AI applications to showcase to employers. Work that gets you hired.
Interview Prep & Mock Interviews
Practice technical and behavioral interviews. Learn what hiring managers look for.
Resume & LinkedIn Optimization
Transform your online presence to attract recruiters. Stand out from other applicants.
Community Career Support
Join the AI Native Engineer community. Not seeing results yet? You stay and keep going. We're with you through the ups and downs.
Voice AI Is Growing Faster Than the Talent Pool. Specialists Are in High Demand.
Every month you delay can cost you thousands in lost earning potential. While you're watching tutorials, others are landing $120K+ AI Engineering roles.
I can only work with a limited number of 1:1 clients at a time to ensure you get the personalized attention you deserve.
Frequently Asked Questions
What does a Voice AI Engineer actually do?
Voice AI Engineers build systems that understand and generate human speech. Common projects: AI receptionists that handle incoming calls, customer service voice agents, voice-enabled search and assistants, call center automation, accessibility tools, and voice-controlled applications. You'll work with speech-to-text (Whisper, Deepgram), text-to-speech (ElevenLabs, PlayHT), telephony integration (Twilio, Vonage), and LLMs for conversation handling. The focus is real-time, natural-sounding interactions.
What skills do I need for voice AI engineering?
Core skills: Python programming, LLM APIs (OpenAI, Claude), prompt engineering for conversation. Speech-specific: STT/TTS APIs, audio processing, handling accents and noise. Systems: WebSockets, streaming, real-time architecture, low-latency optimization. Domain: telephony systems, call flow design, interruption handling. Voice AI requires understanding both the AI and the audio engineering sides. You're building systems where 200ms latency feels slow.
What do Voice AI Engineers earn?
Entry-level Voice AI Engineer: $100K-$140K (1-2 years). Mid-level: $140K-$180K (3-5 years). Senior Voice AI Engineer: $180K-$220K (5+ years). Lead/Principal: $200K-$280K+. Specialized roles at voice-first companies often pay 10-20% premium. Contract rates: $100-$175/hour. Voice AI is niche enough that experienced specialists command strong compensation, especially for production-proven experience.
How is Voice AI different from general AI engineering?
Voice AI requires real-time processing—you can't make users wait 3 seconds for a response in conversation. You need to handle: audio quality issues, background noise, accents, interruptions, and turn-taking. Latency is critical—every optimization matters. Text-based AI is more forgiving. Voice AI also involves telephony systems, audio codecs, and voice synthesis quality. It's a superset of AI engineering with specialized requirements.
How do I start in Voice AI?
Path 1: Add voice to existing AI skills. If you already do LLM development, learn STT/TTS APIs and build a voice chatbot. Path 2: Learn the stack end-to-end. Start with Whisper for transcription, add OpenAI for conversation, add ElevenLabs for speech output. Path 3: Build practical projects. Create a voice agent that can handle phone calls—this demonstrates the full skill set. The key is building something end-to-end, not just learning individual components.
What tools do Voice AI Engineers use?
Speech-to-text: Whisper (OpenAI), Deepgram, AssemblyAI. Text-to-speech: ElevenLabs, PlayHT, Amazon Polly. Telephony: Twilio, Vonage, Plivo. Voice agent platforms: Retell AI, Vapi, Bland AI. LLMs: OpenAI, Claude, Gemini. Streaming: WebSockets, WebRTC. Processing: FFmpeg, audio codecs. Python frameworks: FastAPI for APIs, asyncio for real-time. Most voice engineers build custom solutions combining these tools rather than using all-in-one platforms.
What background do I need for voice AI?
Ideal backgrounds: AI/ML engineer wanting to specialize, backend developer with API experience, audio/telephony engineer adding AI skills. You need solid Python and API development skills as foundation. Audio processing knowledge is valuable but can be learned. If you're completely new to programming, build general AI skills first, then specialize. Voice AI is a specialization on top of AI engineering fundamentals.
How long does it take to become a Voice AI Engineer?
From AI engineer: 2-4 months to add voice specialization (learn speech APIs, build projects). From backend developer: 4-6 months (learn LLMs, then add voice). From scratch: 8-12 months (programming fundamentals, AI skills, then voice specialization). Voice AI is a specialization—you need the AI engineering foundation first. Building 2-3 voice agent projects demonstrates competence to employers.
What if I don't land interviews in 90 days?
You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.
How is this different from online courses?
Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.
What's the investment for 1:1 coaching?
Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.
Can I do this while working full-time?
Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.
Ready to Land Your AI Role?
Stop watching others succeed. Start building your AI career today.
30-minute strategy call • Limited spots available