Lead MLOps Engineer @ hubQuest

3 dni temu

Warsaw, Polska hubQuest Pełny etat

You are invited to join our partner's Global Analytics unit which is a global, centralized team with the ambition to strengthen data-driven decision-making and the development of smart data products for day-to-day operations.
Currently we are looking for a Lead MLOps Engineer as we are supporting our partner in developing Global Analytics unit which is a global, centralized team with the ambition to strengthen data-driven decision-making and the development of smart data products for day-to-day operations.
Team's essence is an innovative spirit, which permeates throughout the company, nurturing a data-first approach in every facet of the business. From sales and logistics to marketing and purchasing, our smart data products have been pivotal in rapid growth and operational excellence. As team expands its analytics solutions on a global scale, we are on the lookout for an experienced Lead MLOps Engineer.
The Global Analytics team is a diverse collective of Data Scientists, Data Engineers, Business Intelligence Specialists, and Analytics Translators, with footprints across three continents and five countries. Its ethos revolves around fostering collaboration, driving innovation, and ensuring reliability. Together, we're committed to transforming whole organization into a leader in data-driven decision-making, leveraging global diversity to tackle challenges and create value.
The team has a lot of freedom to shape this, especially in the use of tools and technology, but also by introducing new concepts, solutions and ways of working.

advanced degree in Computer Science, Statistics or related STEM field
at least 5 years ML engineering experience or engineering support for data science experience in an industrial / commercial setting
experience using Azure cloud services and infrastructure
experience in the operationalization of Data Science projects using Azure
experience in deploying ML models to production and managing them
advanced Python programming skills along with knowledge of Python ML stack
advanced knowledge of DevOps tools and methods such as Docker, Kubernetes, CI/CD pipelines
ability to create and develop in CI/CD pipelines which allow for controlled and continuous enhancement of existing work and new features during both development and production phases
good understanding of machine learning algorithms, ML and AI concepts, and hands-on experience in ML model development
experience building and optimizing data pipelines, architectures and data sets
strong software engineering skills (including unit testing, OOP)
experience with working with large data sets through Spark and RDBMs
experience with versioning systems (e.g. Git)
ability to write quality, well-documented and well-tested code for software services
experience in deploying to production and ability to plan a product including broader technical landscape (ability to design and deliver product following modular approach)
experience working in a start-up environment or organizations with the agile culture, in cross-functional teams
professional attitude and service orientation
team player
ability to work autonomously to deliver complex projects
fluent English as you will communicate in English almost all the time
You are invited to join our partner's Global Analytics unit which is a global, centralized team with the ambition to strengthen data-driven decision-making and the development of smart data products for day-to-day operations.
Currently we are looking for a Lead MLOps Engineer as we are supporting our partner in developing Global Analytics unit which is a global, centralized team with the ambition to strengthen data-driven decision-making and the development of smart data products for day-to-day operations.
Team's essence is an innovative spirit, which permeates throughout the company, nurturing a data-first approach in every facet of the business. From sales and logistics to marketing and purchasing, our smart data products have been pivotal in rapid growth and operational excellence. As team expands its analytics solutions on a global scale, we are on the lookout for an experienced Lead MLOps Engineer.
The Global Analytics team is a diverse collective of Data Scientists, Data Engineers, Business Intelligence Specialists, and Analytics Translators, with footprints across three continents and five countries. Its ethos revolves around fostering collaboration, driving innovation, and ensuring reliability. Together, we're committed to transforming whole organization into a leader in data-driven decision-making, leveraging global diversity to tackle challenges and create value.
The team has a lot of freedom to shape this, especially in the use of tools and technology, but also by introducing new concepts, solutions and ways of working.

,[understanding business problems and designing smart data products in cooperation with Data Scientists, designing, implementing and improving MLOps frameworks for Data Science projects following best practices, designing and developing Azure based cloud infrastructure for developing and deploying AI-driven applications, working on designing and building Azure cloud hosted, automated pipelines that run, monitor and retrain data science models for business applications, supporting Data Engineers in data ingestion and processing workflow automation, creating and maintaining workflows for training, testing and deploying data science models to production environment in close collaboration with Data Scientists and Data Engineers, supporting life cycle management of deployed ML applications (e.g. new releases, change management, monitoring and troubleshooting), participation in implementing frameworks for measuring and optimizing the quality of deployed solutions] Requirements: Python, MLOps, Kubernetes, Docker, Spark, DevOps, DevOps tools, Azure, Machine learning Tools: Agile, Scrum. Additionally: Sport subscription, Private healthcare, Training budget, Small teams, International projects, Free coffee, Bike parking, Free beverages, In-house trainings, In-house hack days, Modern office, No dress code.

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