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You will work with other engineers and data scientists to solve some of the hardest business problems. You will learn what it takes to build, deploy & scale Machine Learning/Artificial Intelligence models in real-world (it’s a hard problem, we assure you). You will build analytical data sets on which model’s will be built. This would entail two key tasks – feature engineering on large datasets and optimization of our pipelines to drive scalability. If you are already good at it, we will make you better.
You will:
Build and maintain automated processes for deploying and managing machine learning models including batch jobs, inference APIs and model lifecycle maintenance using mlFlow.
Build ML Pipelines.
Optimize for efficiency, ensure the models deployed are high performing.
Meet business objectives, automate to reduce manual effort in tasks like model deployment and testing, implement tools and techniques to monitor model performance in production
Essential Requirements•
7- 12 years of overall experience including Design and implementing MLOps pipelines for automating machine learning workflows
Extensive experience in Python, Pivotal Container Service (PKS) or Kubernetes and Docker
Utilize on Prem cloud platform to build and deploy machine learning models
Integrate MLOps tools like MLflow into the machine learning workflow
Monitor and maintain machine learning models in production
Desirable Requirements
Experience working in an agile environment
Strong problem-solving and critical thinking skills
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