Head of ML Engineering Center
8.0/10
Gazprom Neft
Not specified
Hybrid
lead
7 days ago
aidevtechPythonMLOpsDockerKubernetesCI/CDGitSQLBigDataHadoop
AI Summary
The vacancy provides clear responsibilities and tech stack but lacks salary details and company links.
Description
Join Gazprom Neft as the Head of ML Engineering Center to lead a team in developing and deploying ML solutions, with a hybrid work model and comprehensive benefits.
We are developing a digital platform for Gazprom Neft's sales department, launching IT projects, and enhancing customer experience.
The leader in advanced analytics and AI invites you to join as the Head of ML Engineering Center.
## What you'll do
- •Lead a team of MLE engineers through project stages from hypothesis to production deployment.
- •Ensure high-quality architecture and code.
- •Apply ML methods to a wide range of tasks.
- •Design and implement microservice architectures for ML systems.
- •Collaborate with business stakeholders to understand requirements.
- •Coordinate with teams like BI, DWH/Data Lake, and DevOps.
- •Mentor team members to enhance their ML engineering skills.
- •Generate new ideas and identify growth opportunities through ML.
## Conditions
- •Work schedule: 5/2, 09:00 - 18:00, Fri until 16:45.
- •Hybrid work format - office 5 times/month.
- •Employment under Russian labor law.
- •Salary discussed with successful candidate.
- •Annual bonus based on achievements.
- •Health insurance with dental and life insurance.
- •Permanent employment contract.
- •Professional development in a leading IT community.
- •Corporate sports, team tournaments, and runs.
- •Comfortable office with amenities.
- •Family events and support for volunteer initiatives.
Requirements
- •Degree in Computer Science, Applied Mathematics, or related fields.
- •Solid experience in developing and deploying industrial ML systems.
- •Practical knowledge of MLOps principles.
- •Experience with containerization platforms and CI/CD pipelines.
- •Advanced Python knowledge for ML/DS tasks.
- •Practical application of LLM and RAG architectures.
- •Experience with optimization methods.
- •Experience with queue systems, caching, and distributed computing.
- •Proficiency in SQL and BigData technologies.
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