Position: Lead MLOps Engineer
Company: Soft Serve
Experience required: 3y
English level: intermediate

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. You will help us to design and implement ML end-to-end solutions, create data pipelines and architectures, set up the infrastructure, and optimize existing models.

You should be strongly competent in software engineering, have a solid knowledge of Machine Learning/Deep Learning models and workflows, and a good understanding of DevOps/MLOps principles.

A candidate should demonstrate such experience and abilities as

MS degree in computer science or related field
4+ years of relevant experience as ML Engineer or similar
Solution architecture design and ability to articulate complex architectures to a non-technical audience
Hands-on experience in ML operationalization
Strong knowledge of Python and traditional Python DS/ML stack
Working experience with container technology and orchestration platforms such as Kubernetes
Knowledge of any major cloud platform such as GCP, AWS, Azure, or IBM
Setting up CI/CD/CT pipelines
Strong requirements gathering and estimation
Upper-Intermediate English level or higher
Your extra power reveals in the following experience

Hands-on experience with Kubeflow, MLflow, or similar
Hadoop ecosystem and Apache Spark
Workflow orchestration platforms such as Airflow
Designing and building feature stores
Message queues and streaming platforms
R, Java/Scala, Julia
Edge AI
YOU WANT TO WORK WITH

Communication with stakeholders to identify use cases, gather requirements, and set up expectations
Guiding Engineering and Data Science teams on ML systems production lifecycle
Educating Product teams on best practices for putting ML systems in production
Collaborating with Data Science teams on model operationalization strategies
Product teams closely to deliver and operate ML systems
Designing and 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, system monitoring
Designing and implementing automated deployment and integration of ML models
Setting up scalable monitoring systems for data pipelines and ML models
Maintaining ML pipelines in production
TOGETHER WE WILL

Operationalize our clients’ AI solutions by leveraging best practices in DevOps, Machine Learning, and Solution Architecture
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
Get access to 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