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
31-July-24