About this Role
As a Principal Machine Learning Ops Engineer, you'll lead a team dedicated to productionizing machine learning models and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll serve as a technical lead, providing input into machine learning architectural design decisions, reviewing model and application code, and ensuring high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. You’ll also mentor other engineers and further develop your technical knowledge and skills to keep Farmers at the cutting edge of technology.
Essential Job Functions
- Work with teams to design and build cloud hosted, automated pipelines that run, monitor, and retrain ML Models for business applications in an agile manner.
- Enhance and improve the code deployment and model monitoring frameworks and project operations documentation.
- Support life cycle management of deployed ML apps life cycle management (e.g. new releases, change management, monitoring and troubleshooting).
- Excellent communication skills with demonstrated experience driving teams forward and the ability to influence technical decisions to line up with the company’s strategy.
- Work as subject matter expert for app user base (e.g. maintain user guides, release notes, FAQs).
- Build processes supporting app monitoring, troubleshooting, life cycle management and customer support.
- Mentor and coach engineers, helping them grow both their technical and non-technical skill.
- Cultivates innovation by proactively proposing new ideas to deliver business value more effectively.
- A desire to build great things with a great team!
- Performs other duties as assigned.
- High school diploma or equivalent required.
- Bachelor’s Degree in Computer Science, Engineering, Mathematics, Computational Statistics, Data Science or related technical field or equivalent preferred; Master’s Degree preferred.
- 6+ years of experience designing and building data-intensive solutions using distributed computing.
- 4+ years of experience in building and maintaining machine learning engineering pipelines end-to-end in production environments (includes feature engineering, model training, model scoring, model validation, model deployment, test automation, etc.).
- 4+ years of experience in languages like Python, Java, Scala and SQL skills.
- Fundamental understanding of microservices and distributed systems architecture.
- Experience in MLOps using at least one of the popular frameworks or platforms (e.g., Kubeflow, MLFlow, AWS Sagemaker, DataRobot).
- Understanding of AWS networking, security, IAM roles, monitoring and application debugging is critical.
- Experience specifying infrastructure to be built using tools such as Terraform or Jenkins.
Special Skill Requirement
- Experience in insurance or financial services industry.
- Experience supporting Customer Journey Analytics.
- Experience with Algorithmia.
- Experience in the operationalization of Data Science projects (MLOps) using AWS.