Tiger Analytics

Tiger Analytics is a leading advanced analytics consulting firm partnering with Fortune 100 companies to drive business value through expertise in marketing science, customer analytics, and operations analytics.

Professional Services

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1K-5K (3314)

Founded 2011

71 open positions

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MLOps Engineer(NJ)

2 months ago

United States

Full-time

Senior

Machine Learning Engineer

Software Development

Apache Airflow Apache Spark AWS AWS SES CloudFormation Docker FastAPI Flask Hadoop Keras Kubernetes Machine Learning MLflow NumPy Oracle Pandas Pytest Python REST API SageMaker Scikit-learn SciPy TensorFlow Terraform

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Tiger Analytics

Professional Services

1K-5K

Founded 2011

View All Jobs 71

Description

Build, deploy, test, and monitor machine learning training models and scoring pipelines using AWS SageMaker and related AWS services.
Develop Airflow DAGs to orchestrate training and scoring workflows.
Create a testing framework using Pytest for machine learning pipelines and experiments.
Implement monitoring solutions using Lambda and Dash.
Develop data quality checks and solutions, potentially using Great Expectations.
Collaborate with Data Engineers and Data Scientists to build data and model pipelines.
Support machine learning tests, model evaluation, and experimental design.
Work within continuous integration practices and maintain good coding standards.
Contribute to cloud-based ML infrastructure using tools such as CloudFormation, Terraform, Docker, and Kubernetes.

Requirements

Bachelor's degree or higher in computer science or a related field.
5+ years of work experience; 5-7 years of IT experience preferred for the ML engineer role.
Experience with AWS SageMaker, including Processing Jobs, Training Models, and EndPoints.
Experience with Lambda, CloudFormation or Terraform, Apache Airflow, Astronomer, and Docker.
Knowledge of traditional machine learning models.
Strong Python skills with experience in Spark, Hadoop, and Docker.
Experience with good coding practices in a continuous integration context.
Knowledge of ML frameworks such as Scikit-learn, TensorFlow, and Keras.
Experience with Pandas, NumPy, SciPy, and sklearn.
Knowledge of database and data engineering concepts.
Experience with Oracle, Spark, Hadoop, Athena, APIs, FastAPI, Flask, and REST services.
Knowledge of MLflow, Airflow, and Kubernetes.
Experience with cloud environments and AWS services such as Service Catalog, SNS, and SES.

Benefits

Opportunity for significant career development.
Fast-growing and challenging entrepreneurial environment.
High degree of individual responsibility.

Interested in this position?

Apply directly on the company website

Apply Now

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