We are looking for a Voice AI Engineer (Real-time speech) to join our team!
Our client is an Azerbaijani telecommunications company and Azerbaijan's largest mobile network operator. The main products are: Fixed telephony, Mobile telephony, Internet services, Wireless broadband, and Value-added services. The primary goal is to accelerate the client's Data & AI initiatives via a secure, hybrid cloud foundation on AWS while systematically modernizing the IT estate as part of the AWS MAP 2.0 program.
Key Project Objectives include:
Cloud Foundation & Landing Zone: Deploy target hybrid network architectures, establishing a secure AWS Landing Zone Accelerator (LZA) and hybrid Data\/AI platforms on AWS.
Security, Compliance & Sovereignty: Operationalize on-premises data de-identification and Format Preserving Encryption (FPE) tokenization (achieving zero raw PII in the cloud), fully adhering to Azerbaijani Personal Data Law No. 998-IIIQ and Critical Information Infrastructure Rules (Resolution No. 229).
AI Chatbot & Real-Time Voicebot Implementation: Develop and operationalize a flagship Customer Care Voicebot (STT LLM TTS pipeline) and Agentic Chatbot targeting < 2.0s conversational voice latency and ~200 rps throughput as the first hybrid-setup consumer.<\/p>\n
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.<\/p>\n
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).<\/p>\n
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.<\/p>\n
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).<\/p>\n","protected":false},"excerpt":{"rendered":"
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