ML Ops Engineer

Are you a driven problem solver looking to help our clients tackle some of the most pressing challenges within Government and Public Services (GPS). Join Deloitte's Program Integrity practice to help government agencies protect taxpayer money. To address the threats that perpetuate fraud, waste, and abuse, our clients look to our team to provide the guidance and solutions required to help them stay ahead of emerging issues and protect the integrity of their programs. If you are looking for a rapidly growing, collaborative environment with opportunities to make an impact and grow, our Program Integrity team would be a great fit for you!




Work you'll do




Our team detects situations of fraud, waste, and abuse through reviewing claims. the ML Ops Engineer will be responsible for learning and implementing new infrastructure.




The team




Deloitte's Government and Public Services (GPS) practice - our people, ideas, technology and outcomes-is designed for impact. Serving federal, state, & local government clients as well as public higher education institutions, our team of over 15,000+ professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise.




We bring a rigorous approach to help government agencies effectively detect, prevent, and respond to issues related to fraud, waste, and abuse. Our team helps tackle these threats by bringing cutting edge analytics and AI experience with innovative mindsets. Our Program Integrity team focuses on thought diversity and collaborative problem solving to help clients address these challenges holistically, with a common goal to protect the integrity of their programs.




Qualifications




Required:

• Bachelor's Degree in Economics, Finance, Statistics, Mathematics, Computer Science, Management Information Systems, Engineering, Business Analytics disciplines, or related area
  
• 4-6 Years minimum / no location constraint
  
• 2+ years of experience working in a Data Science/Machine Learning Engineering role.
  
• Proficient in Python, Spark (Pyspark), and SQL
  
• Experience deploying and configuring applications in Kubernetes
  
• Experience automating cloud resource deployment in Terraform. Comfortable operating in a Linux environment.
  
• Experience developing production applications with Big Data, with tools like Spark, Hive, and Hadoop.
  
• Experience building model training pipelines in the cloud.
  
• Experience deploying ML services and applications to at least one major cloud platform (AWS, Azure, GCP, IBM Cloud)
  
• Proficient in software design patterns (e.g. understand object-oriented vs functional programming principals, inheritance, writing abstract, reusable, and modular code)
  
• Experience building and deploying microservices as part of Machine Learning/Data Science applications.
  
• Experience with building continuous integration and delivery pipelines for Machine Learning applications.
  

Preferred:

• Experience with at least one deep learning framework (e.g., TensorFlow, PyTorch, Caffe, MxNET)
  
• Experience developing with AWS managed services such as EMR.
  
• Experience orchestrating the deployment and management of predictive models in a cloud environment.
  
• Experience working in an AGILE development team.