ML Engineer - Data & Advanced Analytics

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, versioning, and data security.  - 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.  - Ability to work in a team environment and seek guidance on tasks from senior developers and leads.  - Regular, predictable, full attendance is an essential function of the job. Must be willing and able to contribute to brainstorming sessions in a meaningful way.  - Willingness to travel as necessary, work the required schedule, work at the specific location required, complete Penske employment application, submit to a background investigation (to include past employment, education, and criminal history) and drug screening are required. 
  Physical Requirements:  - The physical and mental de