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
