toogeza is hiring a
ML Ops Engineer

Summary

The job is for an ML Ops Engineer at PlayA, a Ukrainian iGaming startup. The role involves designing, implementing, and managing the infrastructure for machine learning models, automating deployment, maintaining CI/CD pipelines, ensuring security and scalability, collaborating with data scientists, and troubleshooting issues.

Requirements

• 3+ years of experience in ML Ops, DevOps, or related roles (SRE/Cloud Engineer/Infrastructure Engineer, etc)
• Strong experience with cloud platforms such as AWS, GCP, or Azure (AWS preferred)
• Proficiency in Bash for Linux administration on Virtual Machines
• Proficiency in Python for infrastructure and API Development
• Proficiency with CI/CD tools (GitLab CI, Jenkins)
• Experience with version control systems (Git)
• Experience with SQL for data management
• Experience with AirFlow for process orchestration
• Knowledge of resource management and load balancing solutions
• Experience with containerization technologies (Docker)
• Experience of infrastructure-as-code tools (Pulumi, Terraform)
• Experience with monitoring and logging tools (Prometheus, Grafana, ELK stack, etc.)
• Strong problem-solving skills and attention to detail
• Excellent communication and collaboration skills
• English - at least an Upper - Intermediate level
• Bachelor's or Master’s degree in Computer Science, Engineering, or a related field

Responsibilities

• Design, implement, and manage the environment and infrastructure required for machine learning models training and inference
• Support and automate deployment and orchestration of ML models to production environments
• Develop and maintain CI/CD pipelines for ML models and data pipelines
• Set up logging, monitor and alerting processes of the deployed models and data pipelines
• Ensure the security, reliability, and scalability of our ML infrastructure. Managing load on the servers
• Manage cloud resources efficiently to optimize cost and performance
• Support and automate the platform integration with other (customer’s) platforms
• Collaborate with data scientists and engineers to streamline the model development and deployment process
• Troubleshoot and resolve issues related to ML infrastructure, deployment, and performance

Preferred Qualifications

• Experience with ML frameworks and libraries (Pandas, Scikit-learn, PyTorch etc.)
• Knowledge of data engineering and ETL processes
• Knowledge of Big Data (HDFS, Hive, Spark, etc) ecosystem
• Understanding of data privacy and security principles

Benefits

• Education budget of $600 per year provided
• Business travel expenses covered
• Professional English courses
• Medical Insurance