Sr. Principal Machine Learning Ops Engineer

Key Skills

Apache Hadoop, AWS, Continuous Integration & Continuous Delivery - CI/CD, Databricks, Effective communication skills, Google Cloud Platform (GCP), Kubernetes-K8s, Machine learning techniques, MLflow, MLOps tools, Python Programming

Job Description

The Senior Principal MLOps Engineer role is critical because it plays a key role in the successful deployment, management, and monitoring of machine learning models in production environments. At onetrust we are building several ML Model and deployment and operation in a secure, efficient, and effective manner, enabling onetrust to derive maximum value from their machine learning investments.

Your Mission

• Design the data pipelines and engineering infrastructure to support our clients’ enterprise machine learning systems at scale
• Take offline models data scientists build and turn them into a real machine learning production system
• Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients’ machine learning systems
• Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
• Support model development, with an emphasis on auditability, versioning, and data security
• Facilitate the development and deployment of proof-of-concept machine learning systems
• Build data systems and pipelines
• Automate the deployment and scaling of machine learning models in production
• Monitor and maintain the performance and accuracy of machine learning models in production
• Implement continuous integration and delivery pipelines for machine learning models
• Collaborate with data scientists, software engineers, and other stakeholders to ensure the effective deployment and operation of machine learning models
• Ensure the security, privacy, and compliance of machine learning models and related data
• Stay up-to-date with the latest technology and industry trends in MLOps

You Are:

• Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or or equivalent
• Experience in MLOps tools like MLFlow or Azure Machine Learning
• Strong software engineering skills in complex, multi-language systems
• Fluency in Python
• Experience working with cloud computing and database systems
• Experience building custom integrations between cloud-based systems using APIs
• Experience developing and maintaining ML systems built with open source tools
• Experience developing with containers and Kubernetes in cloud computing environments
• Ability to translate business needs to technical requirements
• Strong understanding of software testing, benchmarking, and continuous integration
• Exposure to machine learning methodology and best practices
• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field
• 12+ years of experience in data engineering or a related field
• Strong analytical skills
• Strong communication and writing skills
• Excellent organizational skills
• 6+ Years experience working with Cloud Platforms (i.e. Microsoft Azure, AWS, GCP)
• 6+ Years experience on Python, Databricks / Hadoop
• 6+ years experience in ML OPS