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Machine Learning Engineer

1 month ago

Bengaluru, India Chubb Full time
The Purpose of the roleThe AI Team at Chubb is looking for a highly motivated and experienced ML Engineer to join our fast-paced, high-energy team responsible for delivering bleeding edge solutions leveraging AI.The ML Engineer will work closely with our Data Engineers, Data Scientists, DevOps team to develop and deploy scalable and robust machine learning models.Ideal candidate for this role is someone with a deep expertise in writing and reviewing production code in Python. Knowledge of ML frameworks and libraries (such as TensorFlow), and exposure to various ML techniques and their practical implementation in production at large scale is must.Location: Bangalore, IndiaResponsibilitiesDevelop and

deploy scalable and efficient machine learning models.Package and publish

codes and solutions in reusable format python package format - (Pypi, Scikit-learn pipeline,..)Keep the code ready

for seamless building of CI/CD pipelines and workflows for machine learning applications.Ensure high quality code

that meets business objectives, quality standards and secure web development guidelines.Building reusable tools to streamline the modeling pipeline and sharing knowledgeBuild real-time monitoring and alerting systems for machine learning systems.Develop and maintain automated testing

and validation infrastructure.Troubleshoot pipelines across multiple touchpoints like CI Server, Artifact storage and Deployment cluster.Implement best practices for versioning, monitoring and reusability.Collaborate with multiple teams like Data Science, DevOps and IT to ensure that whole solution is implemented.Manage project stakeholder expectations and issue communications on progress.Design solutions for managing highly complex business rules within the Azure ecosystem.Stay abreast of emerging trends in MLE and identify opportunities to improve our infrastructure.

Required knowledge, Skills and qualifications:Minimum of 5-7 years of experience in ML engineering with large-scale production systems.Strong experience with Azure cloud computing and containerization technologies (like Docker, Kubernetes).Experience with Python/OOPs programming languages and data science frameworks like (Pandas, Numpy, TensorFlow, Keras, PyTorch, sklearn).Knowledge of DevOps tools such as Git, Jenkins, Sonar, Nexus is must.Building python wheels and debugging build process.Data pipeline building and debugging (by creating and following log traces).Basic knowledge of DevOps practices.Concepts of Unit Testing and Test-Driven development.SDE skills like OOP and Functional programming are an added advantage.Experience with Databricks and its ecosystem is an added advantage.

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