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
About Tiger Analytics Inc.
Tiger Analytics Inc. provides data and AI consulting services that deliver machine learning, analytics, and cloud data engineering solutions for large enterprises across industries, including life sciences. The company engages on strategy and implementation projects, from modern BI and data platforms to MLOps, helping Fortune 100 clients turn data into business outcomes. Founded in 2011 and headquartered in Santa Clara, it is privately held with operations across North America, Europe, and Asia-Pacific.