MLOps DevOps Engineer

Job Description

Role & responsibilities Develop, improve, and expand deployment pipeline infrastructure and services Build and deploy hyperscale machine learning pipelines into production Works closely with the developer teams to provide a self-service test integration, test coverage and quality Collaborate with the Security Engineering and Architecture teams to ensure code meets security requirements and is deployed in alignment with standards and governance processes Debug and resolve the issues developers encounter when attempting to use cloud services, with an eye toward continuously improving developer experience Execute against a vision of continuous integration and delivery that empowers developers Preferred candidate profile Building Infrastructure-as-code for Public Cloud using Terraform Experience in a Dev Ops engineering or equivalent role. Experience developing, enhancing, and maintaining CI/CD automation and configuration management using tools such as Jenkins, Snyk, and GitHub Experience with MLOps and orchestration tools such as Airflow, Kubeflow, Optuna, Mlflow or other similar MLOps tools. Experience with operationalizing and migrating ML models into production at scale Experience developing large scale model inference solutions using a parallel execution framework using Spark, EMR, or Databricks Experience developing complex orchestration and MLOps pipelines stitching together large volumes of data for training and scoring Proficiency with Apache Spark, EMR/DataProc and Cloud based tools (Snowflake, Redshift, EMR, Glue, Step Functions, Lambda, Step functions, AWS Batch, or similar etc.) Experience with ML libraries like H20, scikit learn and deep learning frameworks (PyTorch, TensorFlow, etc.) Familiarity with large scale production systems and technologies; for example load balancing, monitoring, distributed systems, and configuration management Development skills with at least one of the following programming languages: Python (preferred), Node.js, Bash. Knowledge of testing automation and code scanning tools Knowledge and experience with running distributed systems in public cloud environments such as AWS, Azure, and GCP. Knowledge of container orchestration platforms such as Kubernetes Knowledge of CDNs, DNS, web servers, and other supporting Internet services Source code management tools using Git. Experienced with standard Gitflow workflow Familiar with best practices for infrastructure security, reliability, and fault tolerance Passionate about automating and improving processes