at Gazpromneft-Regional Sales.

Key Task: Technical leadership and management of the solution lifecycle, solving key business problems, strategic interaction Main Responsibilities:

• Lead a team of MLE engineers at all stages of project implementation: from hypothesis formulation and research (PoC) to development, industrial deployment (production deployment), monitoring, and support of ML solutions.
• Ensure high quality of architecture, code, and modeling solutions (MLOps practices).
• Apply machine learning methods to solve a wide range of tasks: from time series and text analysis (NLP, LLM) to business process optimization (e.g., warehouse logistics).
• Design and implement high-load microservice architectures for ML systems.
• Closely interact with business customers to deeply understand tasks, formulate requirements, and manage expectations.
• Effectively coordinate work with related teams (BI, DWH/Data Lake, Data Quality, DevOps) to ensure end-to-end project implementation and data quality.
• Team & Expertise Development: Mentor team members, enhancing their technical expertise in ML engineering and the quality of engineering solutions.
• Actively participate in generating new ideas, identify opportunities for business growth through the application of machine learning, and propose technological solutions.

We are looking for a candidate who:

• Higher education in Computer Science, Applied Mathematics, or related technical disciplines.
• Solid experience (over 3 years) in developing and implementing industrial systems using machine learning algorithms, successful experience in bringing ML projects to production.
• Management experience in ML, data science, AI from 1.5 years (managing over 4 employees).
• Experience with containerization platforms (Docker/Kubernetes) and CI/CD pipelines, knowledge of Git workflow.
• Experience with queueing systems (AMQP/RabbitMQ), caching (Redis), and distributed computing.
• Practical knowledge and application of MLOps principles: model/service testing, logging, monitoring, data and code versioning (MLFlow/DVC/ClearML).
• Advanced Python knowledge for ML/DS tasks, knowledge of classical ML methods, frameworks, and libraries.
• Practical application of LLMs and RAG-type architectures.
• Understanding and experience in applying optimization methods (knowledge of GAMS/CPLEX or similar tools is a strong advantage).
• Confident work with SQL, experience with BigData technologies (Hadoop/Hive or similar).

An advantage during selection will be:

• Work experience in domain areas: retail / e-commerce, logistics, manufacturing, process control.

Working Conditions

• Work location: St. Petersburg, Vilensky lane.
• Work schedule: 5/2, 09:00 - 18:00, Fri. until 16:45.
• Hybrid work format - office 5 days/month.
• Employment under the Labor Code of the Russian Federation.
• Salary is discussed with the successful candidate.
• Annual bonus based on overall achievements and individual results.
• Social package.

We await your applications .