MLOps Engineer(NJ)
Tiger Analytics Inc. Open · verified Oct 24, 2026 At a glance
Mid-level ML Platform & Ops role at Tiger Analytics Inc. Remote, United States · full-time · no salary stated · we estimate $146,212–$224,250 across the United States.
Tiger Analytics Inc. has not published a salary for this role. The figure below is our estimate, not theirs.
remoteroles summary, based on the employer's posting.
What you'll do
- Build, deploy, test and monitor machine-learning training and scoring pipelines with AWS SageMaker and related AWS services
- Write Airflow workflows to run model training and scoring jobs
- Create a Pytest-based framework for testing machine-learning systems
- Set up monitoring with Lambda and Dash, and develop data-quality checks that may use Great Expectations
What you bring
- A bachelor's degree or higher in computer science or a related subject, plus at least five years of work experience
- Hands-on AWS experience with SageMaker, Lambda and CloudFormation or Terraform
- Experience building data and model pipelines with data engineers and data scientists, and supporting ML tests and experiments
- Working knowledge of Python, Docker, Airflow and traditional machine-learning models
Who this fits
This mid-level role suits an MLOps engineer who can build and operate model pipelines while working with data engineers and data scientists. The position is remote within the United States.
From the employer
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.
Requirements
- 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
Benefits
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.
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