ML Engineer

Catalyst AI™ is an industry first platform that allows customers to compare, diagnose and manage their fleets using the power of data science and Penske’s deep business knowledge. This game-changing technology lets customers get apples-to-apples comparisons instead of static, aggregated industry benchmarks. Penske is the first to solve this need by leveraging AI and machine learning, robust fleet data, and our unique view on maintenance. This technology not only streamlines the fleet benchmarking process, but also delivers actionable, data-driven recommendations tailored to each customer's unique needs. In this role, you will support Catalyst AI and all the future generations of the product. Working with a diverse team, you will lead the technical design of complex components that support our business critical applications, while mentoring other developers on best practices in an effort to deliver our next generation of innovative solutions to our customers.   Position Summary: As the Machine Learning Engineer working in Penske’s Advanced Analytics team, you will be in a high impact role. You will be supporting multiple businesses and functions within Penske with their data science initiatives. You will play a key role in maturing the AI/ML Ops at Penske organization. This is a great opportunity for someone who has some machine learning experience or planning to switch to machine learning as a career choice. Responsibilities As part of Penske’s Advanced Analytics team you will be responsible for implementation and operationalization of AI/ML models. You will work with other machine learning engineers, data scientists, software engineers and platform engineers to ensure success of the AI/ML implementations at Penske. 
  Major Responsibilities:  - Support data scientists with AI/ML model development and deployment with an emphasis on auditability, versioning, and data security.  - Build and implement applications which makes use of AI/ML models  - Work with data scientists to ensure ML models are performing within the expected ranges of accuracy  - Lead Self Service AI (SSAI) initiatives by supporting the citizen data scientists across Penske - Support AI/ML platforms like Sage Maker, SAS Viya or Dataiku - Design data pipelines and engineering infrastructure to support our enterprise machine learning systems  - Apply software engineering rigor and best practices to machine learning, including AI/MLOPs, CI/CD, automation, etc.  - Facilitate the development and deployment of proof-of-concept machine learning systems.  - Develop and deploy scalable tools and services for our clients to handle machine learning training and inference.  - Take offline models data scientists build and turn them into a real machine learning production system. - Experience in technologies, frameworks and architecture like Java or Python, Angular, React, Spring, Spring Boot, XML, JavaScript, JSON, Application Servers, CI/CD is required.  - Experience using AI/ML platforms such as Sage Maker, SAS Viya or Dataiku to deploy Models is a significant plus but not required.  - Ability to identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems.  - Understanding of the full system development lifecycle.  - Other projects/tasks as assigned.
    Penske Qualifications:  - Bachelor’s Degree in Computer Science/Computer Engineering or equivalent years of experience.  - 1-3 years of experience developing software applications and exposure to Machine Learning  - Experience in technologies, frameworks, architecture, and design patterns.  - Strong coding skills in languages like Python and software engineering best practices.  - Experience in designing and building REST APIs and Microservices is required.  - Experience with Relational Databases, MySQL, In-Memory databases, NoSQL databases and writing SQL queries.  - Experience with AWS cloud technologies is a significant plus.  - Understanding of Machine Learning concepts, MLOps and experience using AI/ML platforms such as Dataiku, Sage Maker, or SAS Viya is a plus but not required.  - As part of Advanced Analytics team you will be responsible for implementation and operationalization of Self-Service AI Models.  - Experience customizing Conversation AI platforms is a plus.  - Ability to identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems.  - Design data pipelines and engineering infrastructure to support our enterprise machine learning systems at scale is a significant plus  - Apply software engineering rigor and best practices to machine learning, including AI/MLOPs, CI/CD, automation, etc. is a significant plus.  - Support model development, with an emphasis on auditability,