Voice AI Engineer (Real-Time Speech) (#5861)

Work type: Office/Remote Technical Level: Senior Job Category: Software Development Project: Leading regional mobile network operator 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

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

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 Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more) Active tech communities with regular knowledge sharing Project: Leading regional mobile network operator Project: Global biopharmaceutical company

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