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