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Voice AI Engineer (Real-Time Speech)

N-iX
New Providence, USA
Mid · 4+ years experience
Discovered 1 months ago
awsbedrockcloudwatchdockereksgit

Full Job Posting

Responsibilities:

  • Design, build, and operationalize end-to-end real-time STT → LLM → TTS (Speech-to-Text / LLM / Text-to-Speech) 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 Format Preserving Encryption (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.

Requirements:

  • 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).
  • Nice-to-have skills:
  • AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect.
  • Hands-on experience with EMR-on-EKS, Apache Iceberg, or MSK (Kafka) streaming pipelines.
  • Soft Skills & Team Fit:
  • Strong critical thinking, problem-solving, and analytical skills with ownership of mission-critical, low-latency deliverables.
  • Excellent communication and collaboration skills to work closely with cross-functional teams (AI Architects, Data Engineers, CC SMEs, and Security/Compliance).
  • Results-oriented, proactive mindset with strong ownership within an Agile / Scrum framework.
  • Upper-Intermediate+ English level (written and spoken).
  • We offer:
  • Flexible working format - remote, office-based or flexible
  • A competitive salary and good compensation package
  • Personalized career growth
  • Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
  • Active tech communities with regular knowledge sharing
  • Education reimbursement
  • Memorable anniversary presents
  • Corporate events and team buildings
  • Other location-specific benefits
  • not applicable for freelancers

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