Kyivstar
Tech / AI / Software
AIEngineer(Speech)
Neural analysis suggests this role is
optimal for Mid candidates.
“AI Engineer (Speech) at Kyivstar. Skills: GenAI, Speech Processing, ML/DL, AI assistants, voice bots, STT, TTS, LLMs. Design, train, and optimize ASR and TTS models for production use. Design and develop AI-powered assistants”
What You'll Achieve.
help partners and clients to receive maximum benefits from these technologies too
Industry & Context.
Working with huge amounts of data at the largest operator in Ukraine
What They're Looking For.
Must Have
4+ years of experience in ML/DL with focus on STT/TTS, Hands-on experience training and deploying models for various speech processing tasks (e. g. , speech classification, speech-to-text, text-to-speech, and speaker diarization), Practical experience building AI applications using LLMs, Python skills with experience in writing scalable software modules, Fundamental knowledge of ML/DL theoretical concepts, Familiarity with RESTful APIs and basic web development principles, Proficient in version control (Git) and containerization tools (Docker)
Nice to Have
Experience with real-time / low-latency speech systems, Experience with distributed data processing and training in multi-node, multi-GPU environments, Experience with LangChain, LangGraph, and FastAPI, Experience with Azure/AWS, Familiarity with PySpark for large-scale data processing, Exposure to Data Science in the Telecom industry
What You'll Do.
and optimize ASR and TTS models for production use
Design and develop AI-powered assistants
Fine-tune and optimize large-scale models for specific business needs
Collaborate with DevOps team to deploy the solution
Plan and manage ML projects
articulate methodologies
communicate results and business impact to stakeholders and customers
How You'll Work.
Team & Collaboration
Collaborate with DevOps team to deploy the solution; communicate results and business impact to stakeholders and customers
Communication Scope
communicate results and business impact to stakeholders and customers
Process & Methodology
Plan and manage ML projects
Full Job Description
## Description Project description As part of the AI transformation strategy, Kyivstar invests resources in the integration of GenAI/Speech technologies into internal processes and day-to-day tasks, but also uses gained experience to help partners and clients to receive maximum benefits from these technologies too.One of the key directions of this strategy is the development of AI assistants and services for external partners and clients. To achieve this, company is looking for AI Engineer (Speech) - specialists in the field of GenAI and Speech Processing with deep ML/DL background and practical skills in developing AI assistants and voice bots. Necessary skills 4+ years of experience in ML/DL with strong focus on STT/TTS Hands-on experience training and deploying models for various speech processing tasks (e.g., speech classification, speech-to-text, text-to-speech, and speaker diarization) Practical experience building AI applications using LLMs Strong Python skills with experience in writing scalable software modules Fundamental knowledge of ML/DL theoretical concepts Familiarity with RESTful APIs and basic web development principles Proficient in version control (Git) and containerization tools (Docker) Would be a plus Experience with real-time / low-latency speech systems Experience with distributed data processing and training in multi-node, multi-GPU environments Experience with LangChain, LangGraph, and FastAPI Experience with Azure/AWS Familiarity with PySpark for large-scale data processing Exposure to Data Science in the Telecom industry Responsibilities Design, train, and optimize ASR and TTS models for production use Design and develop AI-powered assistants Fine-tune and optimize large-scale models for specific business needs Collaborate with DevOps team to deploy the solution Plan and manage ML projects, articulate methodologies, and communicate results and business impact to stakeholders and customers Proposed: Working with huge amounts of data a
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