Real-Time Voice AI Engineer - STT/TTs on AWS (Remote)

Nix; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flexible work format

Remote/Office options

Education reimbursement

Mentorship program

Tech talks & trainings

Job summary

Nix is seeking a Voice AI Engineer (Real-time speech) to design, implement, and operate end-to-end real-time voicebot pipelines on AWS. You will work with Whisper ASR, Azerbaijani TTS, Bedrock, and SageMaker, building low-latency, scalable solutions integrated with Avaya and Genesys.

The role emphasizes LLMOps, MLOps, data privacy, and near 2-second latency with ~200 rps throughput, using Terraform or AWS CDK for deployments and observability via CloudWatch and Splunk.

Qualifications

• 4+ years of hands-on experience with machine learning and Speech Processing with a primary focus on real-time conversational AI, ASR (STT), and TTS voice pipelines.
• Deep expertise with Amazon SageMaker (real-time GPU inference endpoints, Pipelines, Feature Store, Model Registry) and Amazon Bedrock (AgentCore, Bedrock Guardrails, Knowledge Bases).
• Proven track record in streaming speech inference, speech synthesis, and low-latency audio processing.
• Strong experience in GPU optimization and containerized orchestration (NVIDIA A100/L40S, AWS EC2 GPU instances, Docker, Kubernetes/EKS).
• Solid understanding of contact center and telephony platform integrations (Avaya, Genesys) and real-time decisioning interfaces.
• Proficient in Python, Redis (priority queuing & caching), and data security/privacy (FPE tokenization, handling sensitive/PII data).

Responsibilities

• Design, build, and operationalize end-to-end real-time STT LLM TTS voicebot pipelines on AWS, optimizing for streaming speech-to-text, first-token LLM generation, and first-audio TTS synthesis.
• Deploy and maintain production customer-trained Whisper (Azerbaijani ASR) and Azerbaijani TTS models as low-latency real-time endpoints on Amazon SageMaker and specialized GPU node pools (NVIDIA A100/L40S).
• Implement and manage the Bedrock Proxy Gateway on EKS for multi-model routing, priority queuing via Redis Sorted Sets, cost caps, and high-availability serving targeting ~200 rps without API throttling.
• Integrate voicebot and chatbot decision engines with core enterprise telephony and CVM platforms, including Avaya (voice telephony), Genesys (digital chat/omnichannel), and Pelatro (CVM offer decisioning and uplift models).
• Establish LLMOps & MLOps pipelines using Amazon SageMaker Pipelines and MLflow for experiment tracking, model versioning, prompt/agent registries, automated evaluation harnesses, and RAG knowledge base retrieval.
• Build call and chat transcription pipelines to ingest, transcribe, and extract real-time insights (churn risk, dissatisfaction, intent, lead signals) into downstream decision layers.
• Enforce data sovereignty and privacy controls by integrating on-premises FPE and tokenization wrappers into ML pipelines so zero raw PII enters AWS cloud environments.
• Define NFR baselines, dialogue flows, voicebot persona, turn-taking, and fallback/escalation logic to guarantee conversational round-trip latency < 2.0 seconds.
• Automate ML deployment workflows using GitLab CI/CD and Infrastructure-as-Code (Terraform or AWS CDK), establishing observability and FinOps spend/anomaly monitoring via Amazon CloudWatch and Splunk.

Skills

Real-time ML

ASR STT

TTS

SageMaker

Bedrock

GPU optimization

Kubernetes

Docker

Python

Redis

FPE tokenization

Security/privacy

Telephony integrations

Tools

Terraform

AWS CDK

MLflow

GitLab CI/CD

CloudWatch

Splunk

Job description

Nix is seeking a Voice AI Engineer (Real-time speech) to design, implement, and operate end-to-end real-time voicebot pipelines on AWS. You will work with Whisper ASR, Azerbaijani TTS, Bedrock, and SageMaker, building low-latency, scalable solutions integrated with Avaya and Genesys.

The role emphasizes LLMOps, MLOps, data privacy, and near 2-second latency with ~200 rps throughput, using Terraform or AWS CDK for deployments and observability via CloudWatch and Splunk.