Responsibilities

Executing clean experiments rigorously against pertinent performance guardrails and analyzing performance metrics to infer actionable findings.
Developing and maintaining services with proactive monitoring can incorporate best industry practices for optimal service quality and risk mitigation.
Breaking down complex projects into actionable tasks that adhere to set management practices and ensure stakeholder visibility.
Managing the end-to-end lifecycle of large-scale ML projects from data preparation, model training, deployment, monitoring, and upgradation of experiments.
Leveraging a strong foundation in ML, statistics, and deep learning to adeptly implement research-backed techniques for model development.
Staying abreast of the best ML practices and developments in the industry to mentor and guide team members.

Requirements

3-5 years of experience in building, deploying, and maintaining ML solutions.
Extensive experience with Python, Sql, Tensorflow/Pytorch, and at least one distributed data framework (Spark/Ray/Dask).
Working knowledge of Machine Learning, probability and statistics, and Deep Learning Fundamentals.
Experience in designing end-to-end machine learning systems that work at scale.