MLOps Engineer(NJ)

Work set-up: 
Full Remote
Contract: 
Experience: 
Mid-level (2-5 years)
Work from: 

Offer summary

Qualifications:

Bachelor's degree or higher in computer science or related field., 5+ years of experience in IT, specifically in machine learning and data science., Proficiency in Python, Spark, Hadoop, and Docker, with good coding practices., Experience with AWS services such as SageMaker, Lambda, and CloudFormation..

Key responsibilities:

  • Pipeline training, building, deployment, testing, and monitoring of ML models using AWS tools.
  • Developing Airflow DAGs for training and scoring pipelines.
  • Creating testing frameworks with Pytest and monitoring solutions with Lambda and Dash.
  • Implementing data quality solutions leveraging tools like Great Expectations.

Tiger Analytics logo
Tiger Analytics XLarge http://www.tigeranalytics.com
1001 - 5000 Employees
See all jobs

Job description

Tiger Analytics is looking for experienced Machine Learning Engineers to join our fastgrowing 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 topnotch talent as we continue to build the best global analytics consulting team in the world. You will be responsible for:

  • ML Engineer with 57 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.
    • Requirements

      • Bachelors 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 DatabaseData 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
            • Benefits

              This position offers an excellent opportunity for significant career development in a fastgrowing and challenging entrepreneurial environment with a high degree of individual responsibility.



Required profile

Experience

Level of experience: Mid-level (2-5 years)
Spoken language(s):
English
Check out the description to know which languages are mandatory.

Other Skills

  • Collaboration

Field Engineer (Solutions) Related jobs