Engineering Manager (MLOps)

Skills

About the Role

You will lead and guide the development of the next generation of MoneyLion's machine learning platform and infrastructure. You'll drive initiatives that enable deployment of machine learning solutions at scale, proactively designing, prototyping, and continuously refining the underlying machine learning infrastructure to remove bottlenecks and expedite the modeling lifecycle. You'll also help define, establish, and enforce best practices in partnership with AI DevOps, Data Scientists, Data Engineers, and AI Engineers across MoneyLion.

Requirements

• Strong experience managing teams of Software Developers
• Strong software fundamentals with experience in software development standards and best practices such as code pattern, code review, test-driven development, and unit testing
• Experience with deploying and managing data pipelines and machine learning algorithms at scale
• Demonstrated practical experience with Kubernetes, with a preference for EKS, in a production environment, and in-depth understanding of Kubernetes architecture and its components
• Ability to evaluate and implement best practices and new technologies to enhance application performance, accuracy, and cost efficiency
• Proficient in Python, Java, SQL
• Strong problem-solving and troubleshooting skills and effective communication and collaboration abilities
• Ability to design and ship applications that provide excellent user experience to data analysts and scientists
• Ability to maintain multiple codebases and conduct code reviews or pair programming to ensure high-quality code
• Familiarity and experience with MLOps tools such as DVC, MLFlow, Metaflow, Ray, Chalk preferred

Responsibilities

• Lead a team of MLOps Engineers in researching, designing, and prototyping MLOps practices, tools, and frameworks to expedite the machine learning lifecycle
• Collaborate with Data Scientists, Data Engineers, and AI/ML Engineering teams to enhance cross-team workflows and reduce time to deployment
• Improve the level of automation in machine learning development and deployment processes
• Collaborate closely with IT operations and DevOps teams to ensure smooth integration of infrastructure platforms with other applications and processes
• Establish and maintain systems for monitoring machine learning models in production
• Oversee the development of machine learning models to ensure proper model governance
• Manage cloud infrastructure costs by monitoring and optimizing spending, and provide transparency and accountability in cost-related matters
• Develop and implement disaster recovery and business continuity plans for cloud infrastructure platforms
• Ensure dependability of the MLOps platforms and solutions, including participating in an on-call rotation to address critical incidents