Expected, AWS, Python
Operating system, Windows
About the project, Transforming the way thousands of global organizations do business by developing the most innovative technologies and processes in Big Data, the 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!
Your responsibilities, Design and implement ML end-to-end solutions, create data pipelines and architectures, set up the infrastructure, and optimize existing models, Communicate use cases, requirements, and expectations with stakeholders, Guide Engineering and Data Science teams on ML systems production lifecycle, Data Science teams on model operationalization strategies, Deliver and operate ML systems cooperating with the product teams, Implement end-to-end production pipelines for ML solutions, Support and enhance ML software infrastructure: CI/CD, data stores, cloud services, network configuration, security, and system monitoring, Set up scalable monitoring systems for data pipelines and ML models, Work as a consultant with the latest modern tools on different projects with a flexible schedule
A holder of a Master’s degree in Computer Science or a related field, Having 3+ years of relevant experience, Hands-on with Python and traditional Python DS/ML stack, An expert in ML solutions design patterns and operationalization, Skilled with AI/ML and Data Engineering tools in any major cloud platform: GCP, AWS, or Azure, Familiar with setting up CI/CD/CT pipelines for ML, Well-versed in Kubeflow, MLflow, or similar, Competent with containers and container orchestration platforms (Kubernetes), Strong in requirements gathering and estimation, Experienced with designing and building feature stores, Knowledgeable of the Hadoop ecosystem and Apache Spark, Familiar with Workflow orchestration platforms such as Airflow, Demonstrating Upper-Intermediate English level, or higher to cover daily project needs
What we offer, Establish trust and build relations with many stakeholders, Have a multicultural team with an open IT mindset rather than a ‘banking type’, Be able to propose and implement solutions, Support your technical and personal growth – we have a dedicated career plan for all roles in our company, Share many other advantages: attractive salary, modern office, a package of benefits, language classes and more
Benefits, sharing the costs of sports activities, private medical care, sharing the costs of foreign language classes, sharing the costs of professional training & courses, life insurance, flexible working time, integration events, no dress code, extra social benefits
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, and Machine Learning pipelines, and integrate them into existing business environments, Participate in international events, Get certifications in cutting-edge technologies, Access strong educational and mentorship programs, Communicate with the world-leading companies from our logos portfolio
SoftServe, SoftServe is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment regardless of race, color, religion, age, sex, nationality, disability, sexual orientation, gender identity and expression, veteran status, and other protected characteristics under applicable law. Let’s put your talents and experience in motion with SoftServe.
This is how we work,
kubeflow CI/CD Artificial intelligence (AI) Python Apache Spark Amazon Web Services (AWS) Data Engineering Azure Google Cloud Platform (GCP) Airflow mlflow Kubernetes Windows Machine Learning Hadoop