Data Platform Engineering Team is responsible for sourcing, transforming, and curating data from structured and unstructured data sources. We are currently on an exciting journey to build unstructured data curation and transformation solution to enrich the consumption of unstructured data for business needs. The Enterprise data platform team has a combination of data scientists, machine learning engineers (MLEs) and data engineers. MLEs would be supposed to implement sophisticated machine learning models that can transform unstructured data into a consumable form for business users.

5– 9 years of overall experience in machine learning with proven experience in deep learning model training and deployment 4+ Years of strong hands-on experience in PyTorch, Hugging face and good understanding of optimizers, learning rate schedulers, etc. Experience in distributed GPU compute is preferable. 4+ years of hands-on experience with Python programming language. Ability to work well in a collaborative, agile development team with business and development partners. Strong team player who is willing to learn, share, teach and lead depending on the situation. Excellent problem-solving and communication skills Continuously integrate and deploy developed software; modify CI/CD pipeline and scripts as necessary to improve continuous integration practices (Terraform). Experience in Google Cloud Platform (GCS, Big Query, Cloud Run, Cloud Build, Pub/Sub) Design and Build Deep learning models to understand and derive insights from unstructured data (Multi-modal - text, image, audio, etc.) Develop custom transformer architectures to solve business problems.  Understand business needs and come up with an appropriate solution architecture for the use-case Ensure deployment of ML models into production by working closely with data engineers / ML-Ops engineers. Use of test-driven Development and code pairing practices (version control). Collaborate with other product teams on integrations, testing and deployments.