Responsibilities - 

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  Build, Refine and Use ML Engineering platforms and components 

  
  


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  Scaling machine learning algorithms to work on massive data sets and strict SLAs 

  
  


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  Build and orchestrate model pipelines including feature engineering, inferencing and continuous model training 

  
  


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  Implement ML Ops including model KPI measurements, tracking, model drift & model feedback loop 

  
  


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  Collaborate with client-facing teams to understand the business context at a high level and contribute in technical requirement gathering; 

  
  


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  Implement basic features aligning with technical requirements; 

  
  


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  Write production-ready code that is easily testable, understood by other developers and accounts for edge cases and errors; 

  
  


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  Ensure the highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews; 

  
  


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  Write unit tests as well as higher-level tests to handle expected edge cases and errors gracefully, as well as happy paths; 

  
  


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  Uses bug tracking, code review, version control, and other tools to organize and deliver work; 

  
  


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  Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues and dependencies; 

  
  


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  Consistently contribute in researching & evaluating latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions. 

  
  


• 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 the latest technologies through rapid learning, conducting proofs-of-concept, and creating prototype solutions

Qualification & Experience - 

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  2-4 years experience in deploying and productionizing  ML models 

  
  


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  Expertise in crafting ML Models for high performance and scalability 

  
  


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  Experience in implementing feature engineering, inferencing pipelines, and real-time model predictions 

  
  


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  Experience in ML Ops to measure and track model performance 

  
  


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  Experience with Spark or other distributed computing frameworks 

  
  


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  Strong programming expertise in Python, Scala or Java 

  
  


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  Experience in ML platforms like Sagemaker, MLFlow, Kubeflow or other platforms 

  
  


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  Experience in deploying models to cloud services like AWS, Azure, GCP 

  
  


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  Good fundamentals of machine learning and deep learning 

  
  


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  Knowledgeable of core CS concepts such as common data structures and algorithms 

  
  


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  Collaborate well with teams with different backgrounds / expertise / functions. 

  
  

Additional Skills - 

• Understanding of DevOps, CI / CD, data security, experience in designing on the cloud platform


• AWS Solutions Architect certification with an understanding of broader AWS stack


• Knowledge of data modeling and data warehouse concepts


• Willingness to travel to other global offices as needed to work with the client or other internal project teams