Machine Learning (ML)We are seeking an experienced AI Architect to design, build, and scale production-ready AI voice conversation agents deployed locally (on-prem / edge / private cloud) and optimized for GPU-accelerated, high-throughput environments.
You will own the end-to-end architecture of real-time voice systems, including speech recognition, LLM orchestration, dialog management, speech synthesis, and low-latency streaming pipelines—designed for reliability, scalability, and cost efficiency.
This role is highly hands-on and strategic, bridging research, engineering, and production infrastructure.
Key Responsibilities
Architecture & System Design
- Design low-latency, real-time voice agent architectures for local/on-prem deployment
- Define scalable architectures for ASR → LLM → TTS pipelines
- Optimize systems for GPU utilization, concurrency, and throughput
- Architect fault-tolerant, production-grade voice systems (HA, monitoring, recovery)
Voice & Conversational AI
- Design and integrate:
- Automatic Speech Recognition (ASR)
- Natural Language Understanding / LLMs
- Dialogue management & conversation state
- Text-to-Speech (TTS)
- Build streaming voice pipelines with sub-second response times
- Enable multi-turn, interruptible, natural conversations
Model & Inference Engineering
- Deploy and optimize local LLMs and speech models (quantization, batching, caching)
- Select and fine-tune open-source models for voice use cases
- Implement efficient inference using TensorRT, ONNX, CUDA, vLLM, Triton, or similar
Infrastructure & Production
- Design GPU-based inference clusters (bare metal or Kubernetes)
- Implement autoscaling, load balancing, and GPU scheduling
- Establish monitoring, logging, and performance metrics for voice agents
- Ensure security, privacy, and data isolation for local deployments
Leadership & Collaboration
- Set architectural standards and best practices
- Mentor ML and platform engineers
- Collaborate with product, infra, and applied research teams
- Drive decisions from prototype → production → scale
Required Qualifications
Technical Skills
- 7+ years in software / ML systems engineering
- 3+ years designing production AI systems
- Strong experience with real-time voice or conversational AI systems
- Deep understanding of LLMs, ASR, and TTS pipelines
- Hands-on experience with GPU inference optimization
- Strong Python and/or C++ background
- Experience with Linux, Docker, Kubernetes
AI & ML Expertise
- Experience deploying open-source LLMs locally
- Knowledge of model optimization:
- Quantization
- Batching
- Streaming inference
- Familiarity with voice models (e.g., Whisper-like ASR, neural TTS)
Systems & Scaling
- Experience with high-QPS, low-latency systems
- Knowledge of distributed systems and microservices
- Understanding of edge or on-prem AI deployments
Preferred Qualifications
- Experience building AI voice agents or call automation systems
- Background in speech processing or audio ML
- Experience with telephony, WebRTC, SIP, or streaming audio
- Familiarity with Triton Inference Server / vLLM
- Prior experience as Tech Lead or Principal Engineer
What We Offer
- Opportunity to architect state-of-the-art AI voice systems
- Work on real-world, high-scale production deployments
- Competitive compensation and equity (if applicable)
- High ownership and technical influence
- Collaboration with top-tier AI and infrastructure talent
Shubham Vishwakarma
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Full Stack Developer - Averlon