We invite you to join a project for one of our clients, an international company, one of the biggest sports brands in the world.

We seek a highly skilled MLOps Engineer to build automation to enable machine learning engineers to continuously deliver predictive analytics for performance, forecasting, personalization, and inventory optimization. You will work with a team of architects, platform engineers, and machine learning engineers to come up with new and interesting models, test them, and productionalize and scale them in the cloud as APIs, stream processing, or massive batch processing. We are looking for a high-energy individual with experience developing cloud-native applications, APIs, and data pipelines that value automation, testing, and security. You will orchestrate cloud, containers, big data, parallel processing technologies, advanced analytics, machine learning, and deep learning techniques to quantitatively plan product demand, allocate resources, and target the right customers with the best products.

Your tasks

• Create cloud infrastructure for machine learning following the MLOps model
• Develop and implement public and internal APIs
• Design event and streaming-based services
• Build Tests and Build a Pipeline for automated Deployment
• Establish Monitoring and Alerting for custom infrastructure
• Worki with batch data pipelines for machine learning for massive data sets
• Support investigation of new cloud services, software packages/tools, APIs, container management, and distributed systems, to continuously deliver quality analytics and machine learning at scale
• Cooperate with a cross-functional agile team of software engineers, data engineers, ML experts, and others to build new product features
• Enable rapid iteration and product quality through continuous A/B testing
• Design, implement, and maintain solutions/platforms
• Collaborate with platform engineering and architects to utilize best practices and maintain compliance

Requirements

• At least 5 years of demonstrated experience in enabling MLOps
• Experience with system configuration management tools, such as Ansible, Puppet, or Chef
• Knowledge of CI/CD infrastructure using Git, Jenkins, or similar tools
• Proficiency with Docker, Kubernetes, Mesos, containers, and related tools and systems
• Background with AWS components and services, particularly, IAM, ECS/EKS, EMR, S3, and Lambda/Serverless
• Prefer infrastructure as code experience with CloudFormation or Terraform
• Strong Python, shell scripting, and SQL
• Familiarity with Spark, Databricks, Snowflake
• Attention to agile development processes, data-driven development, reliability, and responsible experimentation
• Bachelor’s Degree in computer science, software engineering, or related field preferred