Senior Machine Learning Operations Engineer

Summary

Join Blue River Technology as a Machine Learning Operations (MLOps) Engineer and lead the design, development, and implementation of on-premise and cloud MLOps solutions. You will improve system stability, security, efficiency, and scalability, building efficient data pipelines and model training and deployment systems. Responsibilities include developing monitoring and management tools, driving automation initiatives, and building infrastructure and SDK tooling for data scientists and ML engineers. This 100% remote, full-time position offers a competitive salary ranging from $226,158 to $275,000 per year. Apply at https://bluerivertechnology.com/join-us/.

Requirements

• Master’s degree in Computer Science or related field
• 4 years of related experience
• Application development with object-oriented programming languages, including Python/Java (4 yrs)
• Experience building ETL workflows, data warehouse solutions, and data management using AWS GLUE, Spark, Kafka, RDBMS, HDFS, and BigQuery (4 yrs)
• Experience in developing and maintaining full model lifecycle solutions, including model training, evaluation, inference, deployment, and monitoring using ML frameworks including PyTorch/TensorFlow, workflow orchestration tools including Kubeflow/Airflow, cloud workflow platforms including Databricks/SageMaker, and APM monitoring tools including Grafana and Datadog (4 yrs)
• Build infrastructure and SDK tooling to provide data scientists and ML Engineers with access to specialized data augmentation, curation, and visualization tools for CVML model development (3 yrs)
• Create CI/CD build and release pipelines with GitLab/GitHub/Jenkins for code and model deployment, and using Terraform/CloudFormation for infrastructure deployment (2 yrs)
• Analyze and build job orchestration services to scale machine learning tasks on both on-premises and cloud infrastructure in a cost-effective way, including Kubernetes, Airflow, GCP Composer, and Kubeflow (3 yrs)
• Experience with container orchestration with Kubernetes, microservices architecture, and cloud platforms including AWS and GCP (3 yrs)

Responsibilities

• Lead the design, development and implementation of on-premise and cloud MLOps solutions that support the delivery of machine learning model
• Improve stability, security, efficiency and scalability of systems
• Build scalable and efficient data pipelines and model training and deployment systems
• Develop and maintain monitoring and management tools to ensure the reliability and performance of on-premises MLOps infrastructure
• Drive automation initiatives for model deployment and infrastructure provisioning

Benefits

• This is a 100% remote position
• Full time
• $226,158 - $275,000/year