MLOps Engineer responsible for model training, deployment, monitoring, and collaboration with data teams using AWS and various ML frameworks.
Tiger Analytics is looking for experienced Machine Learning Engineers to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.
We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. You will be responsible for:
- ML Engineer with 5-7 years of IT experience.
- Pipeline Training Models, Building, Deployment, Testing, and Monitoring using AWS SageMaker, AWS CFT, AWS CodePipeline, Lambda, etc.
- Develop Airflow DAGs to run training and scoring pipelines
- Develop a Testing framework with Pytest
- Implement monitoring solution with homebrew solution using Lambda and Dash
- Develop Data Quality solutions potentially leveraging Great Expectations.
- Bachelor's degree or higher in computer science or related, 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
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
Top Skills
Airflow
Aws Cft
Aws Codepipeline
Aws Sagemaker
Docker
Fastapi
Flask
Great Expectations
Hadoop
Keras
Kubernetes
Lambda
Mlflow
Numpy
Pandas
Pytest
Python
Scikit-Learn
Scipy
Spark
TensorFlow
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