Machine Learning Operation

About the Role-

This role is for you if you are passionate about machine learning, data engineering, and building products in general. It will require close collaboration with data scientists, analysts, and software engineers, so a basic understanding of DS stack and development in Python will be useful.

Required Experience: 3-5 years

Following is a representative list of what you will be doing but not limited to what is mentioned.

What You Will Do:

• Collaborate with data scientists to optimize ML models for high-throughput, low-latency use cases.
• Setup infrastructure for Deep Learning/GPT use cases which require scaled-up training with cost optimization.
• Engineer high-reliability, high-performance services for sophisticated ML-driven functionality.
• Suggest and design deployment methods on cloud infrastructure (Kubernetes, cloud function, etc).
• Design and build our reporting, data/model version control, and model deployment stack to help data scientists productionize their models and features faster.
• Build and maintain DS/BI warehouse that serves internal tools and interfaces to improve the productivity of the team and improve the accessibility of our products.
• You would serve as the bridge between the data scientists and software engineers.
• Enable efficient monitoring, maintenance and debugging of production ML models.
• Work with data scientists to make production code more robust in terms of scalability, latency, and compute Key skills required.
• Understanding of code optimization and writing high-performance methods

Must have:

• Self learning attitude, we work with latest/open-source technologies and need people who can take full ownership of any new implementation with minimal supervision.
• Knowledge of version control, proficiency in Python, deployment related best practices and enthusiasm towards MLOps.
• Experience with web services and microservice architectures.
• Cloud Service Provider (GCP/AWS), Kubernetes.
• Scaling ML models in high-throughput and low-latency settings.
• Ability to think through LLD and HLD

Good to have/Willing to learn:

• Data science knowledge and familiarity with ML libraries such as Pandas, Scikit, Tensorflow, XGBoost, Keras etc., will help in taking models to production and serving.
• SQL/Big Data, MLOps knowledge Few references about our work

Few references about our work:

https://www.linkedin.com/posts/naresh-mehta-68a52811_machinelearning-deeplearning-cars24-activit y-6832946990003847168-ZsI7

https://medium.com/cars24-data-science-blog/re-imagining-ds-cars24-4d50b27ceb49

https://medium.com/cars24-data-science-blog/ml-in-production-cars24-part-1-e712f54e20a2

https://medium.com/cars24-data-science-blog/ml-in-production-cars24-part-2-6a717340d8e6

https://medium.com/cars24-data-science-blog/convert-detectron2-model-to-tensorrt-efb3f3cd62b1