Voice AI Engineer (Real-Time Speech)

Location: Europe; Ukraine

Department: Azercell Team

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.

 

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).