Machine Learning Engineer

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Career Techniques Inc.
August 19, 2021
Remote, United States of America
Job Type


The Machine Learning Engineer will be responsible for leading teams in client projects to deliver below:


  • Build, Refine and Use ML Engineering platforms and components
  • Scaling machine learning algorithms to work on massive data sets and strict SLAs
  • Build and orchestrate model pipelines including feature engineering, inferencing and continuous model training
  • Implement ML Ops including model KPI measurements, tracking, model drift & model feedback loop
  • Collaborate with client facing teams to understand business context at a high level and contribute in technical requirement gathering
  • Implement basic features aligning with technical requirements
  • Write production-ready code that is easily testable, understood by other developers and accounts for edge cases and errors
  • Ensure highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews
  • Write unit tests as well as higher level tests to handle expected edge cases and errors gracefully, as well as happy paths
  • Uses bug tracking, code review, version control and other tools to organize and deliver work
  • Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues and dependencies
  • Consistently contribute in researching & evaluating latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions


  • 3+ years’ experience in deploying and productionizing ML models
  • Bachelor’s degree
  • Expertise in crafting ML Models for high performance and scalability
  • Experience in the following:
    • implementing feature engineering, inferencing pipelines and real time model predictions
    • ML Ops to measure and track model performanceSpark or other distributed computing frameworks
    • Strong programming expertise in Python, Scala or Java
    • ML platforms like Sagemaker, MLFlow, Kubeflow or other platforms
    • deploying models to cloud services like AWS, Azure, GCP
  • Good fundamentals of machine learning and deep learning
  • Knowledgeable of core CS concepts such as common data structures and algorithms
  • Collaborate well with teams with different backgrounds / expertise / functions.

A plus

  • Understanding of DevOps, CI / CD, data security, experience in designing on cloud platform
  • Experience in data engineering in Big Data systems
  • Willingness to travel to other global offices as needed to work with client or other internal project teams
  • Max. file size: 300 MB.
  • Please complete the math question to prove you are human.

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