Senior MLOps Engineer (offline)

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 best and oldest Data Science teams in Ukraine and you will get tons of experience working with the best talents in the field.

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

YOU ARE
MLOps Engineer with MS degree in computer science or related field and 4+ years of relevant experience
Experienced with solution infrastructure design
Familiar with the Data Science systems and workflow, esp. with all aspects around model operationalization
Proficient in distributed systems principles
Experienced with engineering best practices, including analyzing, designing, developing, deploying, and supporting solution infrastructure implementations and upgrades
Skilled in building and using data systems at scale
Experient in GCP, AWS or Azure beyond basic provisioning, especially data pipeline components, CI/CD, k8s, and ML-relevant managed services
Showing a mastery of container technology and orchestration platforms using Kubernetes
Expertised with building CI/CD pipelines using Gitlab, Jenkins, Azure DevOps, Google Cloud Build, or similar
Able to perform high-quality performance analysis
Skilled in Python, SQL, and Shell scripting
Practiced in Networking, TCP/IP, and Linux administration
Strong in requirement gathering and estimation
Able to articulate complex architecture to non-technical audiences
Able to recognize Data Mining/Machine Learning workflow patterns and solutions associated with those patterns
The one who possesses excellent organization, time-management, presentation, and communication skills
Demonstrating Upper-Intermediate English level or higher (oral/written)

Your extra power reveals in the following experience
Kubeflow, MLFlow, Argo Workflows
Workflow management platforms such as Airflow
Databases and object stores such as PostgreSQL, Redshift, Athena, BigQuery, S3 or similar
Storage design Knowledge of Java/Scala
Hadoop ecosystem, Apache Spark or similar
Stream processing platforms such as Apache Kafka, Cloud Pub/Sub
Developing solutions using automation tools such as Terraform, Ansible, Chef
Visualization platforms such as Tableau
Proficiency with monitoring tools such as Grafana, Prometheus, Stackdriver, Zabbix
A concept of GitOps approach and tools

YOU WANT TO WORK WITH
Your duties will enable you to
Communicate with stakeholders to identify use cases, gather requirements, and set up expectations
Guide Engineering and Data Science teams on ML systems production lifecycle
Educate Product teams on best practices for putting ML systems in production
Collaborate with Data Science teams on model operationalization strategies
Cooperate closely with Product teams to deliver and operate ML systems
Design and implement end-to-end production pipelines for ML solutions
Support and continuously enhance ML software infrastructure: CI/CD, data stores, cloud services, network configuration, security, system monitoring, etc.
Design and implement automated deployment and integration of ML models
Setup scalable monitoring systems for data pipelines and ML models
Maintain 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

About

Yalantis

Company website:
https://yalantis.com/