MLOps Engineer(NJ)
Tiger Analytics Inc.almost 4 years ago
Remote, United StatesSenior
Responsibilities
- Build, deploy, test, and monitor machine learning training and scoring pipelines using AWS SageMaker and related AWS services.
- Develop Apache Airflow DAGs to run model training and scoring pipelines.
- Create a testing framework using Pytest for machine learning pipelines and experiments.
- Implement monitoring solutions using AWS Lambda and Dash.
- Develop data quality solutions potentially leveraging Great Expectations.
- Collaborate with Data Engineers and Data Scientists to build data and model pipelines and run machine learning tests and experiments.
Requirements
- Bachelor’s degree or higher in computer science or a related field, with at least 5 years of work experience.
- 5–7 years of IT experience in machine learning engineering or a related area.
- Experience with AWS SageMaker, including ProcessingJobs, TrainingModels, and EndPoints.
- Experience with AWS Lambda, CloudFormation or Terraform, Apache Airflow, Astronomer, and Docker.
- Knowledge of traditional machine learning models and machine learning frameworks including Scikit-learn, TensorFlow, and Keras.
- Proficiency in Python, Spark, Hadoop, Pandas, scikit-learn, NumPy, and SciPy.
- Knowledge of database and data engineering concepts, Oracle, Athena, FastAPI, Flask, MLflow, and Kubernetes.
- Knowledge of AWS services including Service Catalog, SNS, and SES.
- Understanding of model evaluation, experimental design, coding practices, and continuous integration contexts.
Benefits
- Fully remote, full-time position.
- Opportunity for significant career development in a fast-growing, challenging entrepreneurial environment with a high degree of individual responsibility.
Tech Stack
Apache AirflowApache HadoopApache SparkAWSDockerFastAPIFlaskKerasKubernetesMLflowNumPyPandaspytestPythonscikit-learnSciPyTensorFlowTerraform
Categories
Data EngineeringML Engineering