Machine learning engineer in industrial systems

5 LAB(ФАЙВ ЛАБ); United States; Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Remote work with international team

Growth opportunities

Job summary

5 LAB(ФАЙВ ЛАБ) ищет опытного специалиста по ML/AI для разработки и внедрения интеллектуальных решений в энергетическом секторе. Вы будете заниматься обработкой данных с датчиков, проектированием и валидацией моделей, а также построением масштабируемого API и мониторинга.

Кандидат должен иметь значительный опыт в time-series, anomaly detection и генеративных подходах, уметь работать в кросс-функциональной команде и менторить коллег.

Qualifications

• Мастер/Кандидат наук в области компьютерных наук, Data Science, математики, инженерии или физики.
• 7+ лет опыта создания и развёртывания ML/AI систем в промышленной среде.
• Опыт работы с Python и инженерией ПО, ML-фреймворками, API, тестированием, контейнерами и CI/CD.
• Опыт с временными рядами, обнаружением аномалий или предсказанием, большими неполными данными и надёжной валидацией.
• Навыки MLOps, развертывания в облаке/интеграции, мониторинг и принципы ответственного AI.

Responsibilities

• Р 책임 Lead end-to-end delivery AI-решений, от проверки идеи до развёртывания и мониторинга.
• Интегрировать данные с датчиков и активов через надёжные конвейеры и качественный контроль.
• Проектировать и оценивать современные ML/AI подходы, включая time-series, обучаемые модели и генеративные методы.
• Создавать продакшн-сервисы с API, cloud-native workflows и observability.
• Сотрудничать с предметными экспертами, IT и командами разработки, менторить инженеров и исследователей.

Skills

Python

ML frameworks

APIs

Testing

Containers

CI/CD

Time-series

Anomaly detection

MLOps

Monitoring

Responsible AI

Education

Master's or PhD in CS/DS/Math/Engineering/Physics

Tools

Cloud platforms

Model registries

Container orchestration

Streaming architectures

Job description

Описание:

It is a global energy technology company helping customers generate, transmit and transform energy reliably and sustainably. From power generation and grid technologies to renewable energy, our products, services and digital solutions help drive the energy transition. Join us to build software with real-world impact on the future of energy. You will develop advanced AI/ML solutions for industrial systems, initially focusing on anomaly detection and predictive maintenance.

Задачи:

Lead end-to-end AI solution delivery, from feasibility and data discovery to validation, deployment, and monitoring. Integrate operational, maintenance, engineering, asset, and sensor data through reliable data pipelines and quality controls. Design and evaluate modern AI/ML approaches, including supervised and unsupervised learning, time-series models, deep learning, Generative AI, and RAG. Build production-ready AI services with scalable APIs, cloud-native workflows, MLOps, and observability. Collaborate with domain experts, data owners, IT, cybersecurity, and software teams to deliver measurable outcomes and reusable AI components. Mentor engineers and data scientists, conduct reviews, share knowledge, and contribute to patents and publications where relevant.

Требования:

Master's degree or PhD in Computer Science, Data Science, Mathematics, Engineering, Physics, or a related quantitative field. 7+ years of experience building and deploying ML/AI systems in industrial, infrastructure, energy, or enterprise environments. Strong Python and software-engineering skills, including ML and deep-learning frameworks, APIs, testing, containers, and CI/CD. Experience with time-series data, anomaly detection or prediction, large imperfect datasets, and robust validation for rare or imbalanced events. Hands-on experience deploying ML and Generative AI solutions, including MLOps, cloud or enterprise integration, RAG or agentic patterns, monitoring, and responsible AI practices. Sound technical judgment and communication skills, with the ability to select appropriate methods, collaborate with domain experts, and explain model behavior and trade-offs.

Будет плюсом:

• Industrial operations, complex machinery, operational reliability, predictive maintenance, or comparable asset-intensive domains
• Advanced time-series and rare-event methods, transfer learning, uncertainty estimation, or cross-site model generalization
• Knowledge graphs, semantic or vector search, document intelligence, and linking unstructured evidence with structured operational data
• Cloud AI platforms, model registries, container orchestration, streaming architectures, or delivery of reusable AI platforms

Условия:

Working time Mo-Fr (8 working hours) Employment according labor law of Republic of Kazakhstan Compensation of medical insurance after trial period Remote work with international team Growth possibilities