MLOps Manager

About The Company 

Inchcape is the leading global automotive distributor operating in more than 40 markets. We partner with some of the biggest brands in the business to power better mobility today and in the future. 

Our diverse global team of over 18,000 talented colleagues foster an inclusive and collaborative culture, delivering a brilliant experience for our customers and partners. We’re a dynamic and fast-growing business, driving the transformation of our industry and redefining tomorrow 

The Digital Delivery Centres accelerate Inchcape’s digital journey by positively impacting the business and its customers through innovative and essential technological solutions. 

Through our top talents, we support the mobility of millions of people globally through the power of digital technology. 

Purpose of Role: 

• Serve as a technical guide to the team and contribute to the overall MLOps infrastructure design 
  
• Develop a playbook for MLOps Engineering and set global standards for managing MLOps activities across Inchcape 
  
• Work closely with data scientists, business, and IT teams to build platform and framework to enable machine learning and data analytics activities ensuring robustness & scalability of solutions 

Job Role and Responsibilities: 

• Scope, design, and implement machine-learning models to solve interesting user-cases and achieve measurable improvements 
  
• Continuous innovation and optimization of machine learning workflow, through R&D on modern technologies. 
  
• Collaborate with Data Science teams on model operationalization strategies 
  
• Analytics-as-a-service is your mantra and develop relevant capabilities on the cloud to enable & monitor services based on the principles or Microservices 
  
• Use containerization and cloud technologies (eg. OpenShift, Kubernetes, Docker, Mesos, AWS) as well as DevOps and testing tools (Git, SVN, TFS, Jira, Confluence, Jenkins, Nexus, Selenium) to enable API economy 

Qualifications: 

• Proven track record of leading high-performing data teams that successfully implemented real-time integration utilizing a variety of technologies, development tools and database systems. 
  
• 5+ years of working experience in a hands-on ML role out of which 2+ years of team leadership including people management. 
  
• Master’s degree in Statistics, Machine Learning, Mathematics, Computer Science, Economics, or any other related quantitative field or a bachelor’s degree with at least 4 years of hands-on experience 
  
• You have a strong background in machine learning, theory, and practice. 
  
• You are experienced with building data pipelines, and you are self-sufficient in getting the data you need to build and evaluate your models, using tools like Apache Spark, PySpark or other distributed data processing frameworks. 
  
• Deep knowledge of SOA, Architecture and Design on Micro services, REST Services Development 
  
• Experience using cloud services such as Azure/Databricks is a plus.