MLOps Engineer(NJ)

Posted 16 Days Ago
Hiring Remotely in United States
Remote
5-7 Years Experience
Big Data • Analytics • Business Intelligence • Big Data Analytics
The Role
The MLOps Engineer will be responsible for training, deploying, and monitoring machine learning models using AWS technologies. They will collaborate with Data Engineers and Data Scientists to develop pipelines and build testing frameworks, leveraging tools like Apache Airflow and Docker. They should have deep knowledge of machine learning frameworks and AWS services for effective model evaluation and implementation.
Summary Generated by Built In

Description

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.

Top Skills

Hadoop
Python
Spark
The Company
Bengaluru, Bengaluru
5,000 Employees
On-site Workplace
Year Founded: 2011

What We Do

Tiger Analytics is a global leader in AI and Analytics, helping Fortune 1000 companies solve their toughest challenges. We offer fullstack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow.

Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore, and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare.

We are Great Place to Work-Certified™ and have been recognized by analyst firms such as Forrester, Gartner, Everest, ISG, HFS, and others. Ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine.

In India, our offices are located in Chennai, Hyderabad and Bangalore.

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