About our client: Our client is a global technology company specializing in platform-based digital transformation, supporting businesses to become connected, open, intelligent and scalable. Our client's methodology brings together industry expertise, platform technology excellence, design innovation and strategic engagement models to deliver sustained value to customers. They are a trusted partner of world leaders in the retail, manufacturing, distribution, travel, services and software industries, their software portfolio includes Retail Platform, Modern Distribution, Digital Travel, E-commerce, enterprise development automation, Commodity Trading and Risk Management and AI Powered Customer Experience (CX). Our client services are built on Microsoft Dynamics 365, Microsoft Azure, AWS, Cloud Engineering and Managed Services delivery promise. As world leaders in digital technologies including IoT, Artificial Intelligence, Machine Learning, Robotic Process Automation, Chatbots, Block Chain and Cyber Security, their people and systems are nurtured to deliver on our commitment to excellence in business technology solutions. Requirements MLOps Engineer | Remote 10+ years of IT experience in which at least 6+ years of relevant experience primarily in MLOps, Cloud, Dockerization & Containerization. Develop best practices and how-to guides for MLOps practices relating Git, CI/CD, Input data unit and statistical testing, Experiment tracking, Model Registry, Scheduling of ML pipelines, Production drift monitoring and alerting, Optimization of cloud compute resources Adopt best practices for writing processed data (ML features) to appropriate data lakes, warehouses, or feature stores Extensive experience with infrastructure provisioning and configuring public and hybrid clouds with mandatory GCP experience. Dockerization of Machine Learning scripts and deployment in Google Cloud Platform. Ability to create automatically and deploy Machine Learning notebooks. Also, ability to establish connection with Big Data infrastructure from the Machine Learning notebooks. Experience administering Kubernetes, Google Kubernetes Engine (GKE) and understanding of manifest management with Helm. Experience with CI/CD pipelines and related tools such as Jenkins or CircleCI and Google Cloud Build. Experience with configuration management tools and deployment tools like Terraform and Google Deployment Manager. Good knowledge of other tools like Puppet, Ansible, Chef, Consul, Packer etc. Setup of Monitoring and Alerts for Production workloads in Google Cloud Platform. Experience in automating multiple systems using Bash and languages such as Python and Go. Recommend and implement automated solutions that will improve the performance and reliability of the system. Strong understanding and experience operating in an agile development environment. Strong communication skills across the board, with a passion for finding and sharing best practices and driving greater discipline. Excellent verbal and written communication skills in English. Nice to have: Experience in managing Linux/Unix platforms, application server administration (Tomcat, JBoss, etc.) and DNS, Linux system configuration and administration (CentOS, RedHat, etc.). Good exposure with monitoring tools geared towards user experience and deep diagnostics (Splunk, New Relic, AppDynamics, Prometheus, Grafana, etc.)