Senior Machine Learning Engineer

About the Customer

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.

Your role

You will develop advanced AI/ML solutions for industrial systems, initially focusing on anomaly detection and predictive maintenance. You will combine operational data, maintenance records, engineering knowledge and sensor signals to create reusable capabilities for diagnostics, forecasting and other industrial AI use cases.

As a Senior Machine Learning Engineer, you will turn complex problems into production-ready solutions by building reliable data pipelines, validating results with domain experts and deploying maintainable services that deliver measurable impact.

How you will make an impact

• 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.

What you bring

Must have

• 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.

Nice to Have (Beneficial)

• 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.

Working conditions:

• 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