Cloud AI Engineering Specialist
1 day ago
Tiger Analytics is a dynamic and innovative advanced analytics consulting firm, seeking experienced Machine Learning Engineers to join our team. As a Machine Learning Engineer at Tiger Analytics, you will be responsible for designing, developing, and deploying machine learning models and pipelines using AWS SageMaker, CloudFormation, CodePipeline, Lambda, etc.
- Responsibilities include:
- Pipeline Training Models, Building, Deployment, Testing, and Monitoring
- Developing Airflow DAGs to run training and scoring pipelines
- Creating a Testing framework with Pytest
- Implementing monitoring solutions with homebrew solutions using Lambda and Dash
- Developing Data Quality solutions potentially leveraging Great Expectations.
Requirements:
- Bachelor's degree or higher in computer science or related field with 5+ years of work experience
- Ability to collaborate with Data Engineers and Data Scientists to build data and model pipelines and help run machine learning tests and experiments
- Experience in AWS - SageMaker (ProcessingJobs, TrainingModels, EndPoints)
- Experience in Lambda CloudFormation or Terraform Apache Airflow, Astronomer Docker
- Knowledge of traditional ML Models.
- Python, Spark, Hadoop, and Docker with an emphasis on good coding practices in a continuous integration context, model evaluation, and experimental design
- Knowledge of ML frameworks like Scikitlearn, Tensorflow, and Keras.
- Experience in Pandas, sklearn, Numpy, Scipy
Additional Skills Required:
- Knowledge of Database/Data Engineering.
- Experience with Oracle, Spark, Hadoop, Athena, API, FastAPI, Flask, ReST
- Knowledge of MLflow, Airflow, and Kubernetes
- Experience with Cloud environments and knowledge of AWS Services, Service Catalog, SNS, SES
The estimated salary for this role is around $160,000 - $220,000 per year, depending on location and experience.
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