Senior MLOps Engineer

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Apache Hadoop, Scala Programming, Machine learning techniques, Data science techniques, PyTorch, TensorFlow, MapReduce, R Programming

Job Description

WE ARE

Transforming the way thousands of global organizations do business by developing the most innovative technologies and processes in Big Data, Internet of Things (IoT), Data Science, and Experience Design.

We are one of the largest teams in Eastern Europe that stood at the origins of Data Science, so you will get tons of experience while working with the best talents in the field.

In a Data Science Center of Excellence, you will have a chance to contribute to a wide range of projects in different areas and technologies. We are looking for a person who is inspired by data, analytics, and AI as much as we are, and who wants to grow with us!

YOU ARE

A Machine Learning Engineer who is interested in operationalizing ML pipelines and bringing them to production, possessing

• Strong competence in Machine Learning/Deep Learning models and model lifecycle
• Solid knowledge of software engineering
• Good understanding of DevOps/MLOps principles
• MS degree in computer science or related field 3+ years of relevant experience

Knowledge of

• Python and traditional Python DS/ML stack
• ML solutions design patterns
• AI/ML and Data Engineering tools in any major cloud platform such as GCP, AWS or Azure

Experience in

• ML operationalization
• Setting up CI/CD/CT pipelines for ML
• Kubeflow, MLflow, or similar
• Containers and container orchestration platforms such as Kubernetes
• Designing and building feature stores
• Hadoop ecosystem and Apache Spark
• Workflow orchestration platforms such as Airflow
• Message queues and streaming platforms
• Java/Scala

And such skills as

• Upper-Intermediate English level or higher
• Strong requirements gathering and estimation

YOU WANT TO WORK WITH

• Optimizing existing models
• Communicating use cases, requirements, and expectations with stakeholders
• Guiding Engineering and Data Science teams on ML systems production lifecycle
• Collaborating with Data Science teams on model operationalization strategies
• Product teams to deliver and operate ML systems
• Implementing end-to-end production pipelines for ML solutions
• Supporting and continuously enhancing ML software infrastructure: CI/CD, data stores, cloud services, network configuration, security, and system monitoring
• Setting up scalable monitoring systems for data pipelines and ML models

TOGETHER WE WILL

• Operationalize our clients’ AI solutions by leveraging best practices in Machine Learning and MLOps, DevOps, and Software engineering
• Maintain synergy of Data Scientists, DevOps team, and ML Engineers to build infrastructure, set up processes, productize machine learning pipelines, and integrate them into existing business environments
• Participate in international events
• Get certifications in cutting-edge technologies
• Have the possibility to work with the latest modern tools and technologies on different projects
• Access strong educational and mentorship programs
• Communicate with the world-leading companies from our logos portfolio
• Work as a consultant on different projects with a flexible schedule