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 multitask 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
Standard company Benefits
Machine learning, AWS , Numpy, Pandas