Data Platform Engineering  Team is responsible for Building Data platfrom for enterprise  The ML enablement team has a combination of data engineers, machine learning engineers (MLEs). MLEs would be supposed to implement sophisticated machine learning models that can transform DPE.

• Build Deep learning models to understand and derive insights from 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.

• 4 – 7 years of overall experience in machine learning with proven experience in deep learning model training and deployment
• 2+ 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
• Good to have!
• 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)