Machine Learning Engineer

Key Responsibilities:

•    Design and implement cloud solutions, build MLOps on cloud. Preferably AWS Cloud.
  
•   Build model and data pipelines for Data Scientists and Data Engineers using AWS cloud services.
  
•   Assist in data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its model  quality.
  
•  Hands on experience with different features of AWS SageMaker including but not limited to SageMaker Studio, Jupyter Notebooks, Data Wrangler, Clarify etc.
  
•  Good understanding of the Python ML Libraries. Should be able to prototype and evaluate new libraries and new features available.
  
•  Experience in communicating with Data science team, Cloud Infrastructure team and developers to collect requirements, describe software product features, and technical designs.
  
• Ability and willingness to multi-task and learn new technologies quickly
  
•  Stakeholder management with good Written and verbal technical communication skills with an ability to present complex technical information in a clear and concise manner to a variety of audiences
  

Requirements

•  Proficiency in Python and AWS data services.
  
• Proficiency in AI/ML model operations,
  
•  Experience in building components for data scientists and assisting them in all stages of Model development and deployment. Must have experience in AWS SageMaker and AWS SageMaker Studio
  
•  Experience with IDE/notebook software (Jupyter Studio,, VSCode, PyCharm, etc)
  
• Experience in building data pipeline using on cloud using Cloud technologies (S3, Lakeformation, SQS, SNS, Spark, Glue, Step Functions, Lambda etc)
  
• Good to have experience in Data Visualization tools likeTableau, AWS Quicksight
  

Benefits