Job Description:

• Deep understanding of business/functional needs, problem statements and objectives/success criteria
• Collaborate with internal and external stakeholders including business, data scientists, project and partners teams in translating business and functional needs into machine learning problem statements and specific deliverables
• Act as the ‘Conduit’ between product owners, data scientists, data analysts and data engineers to develop best-fit end-to-end ML solutions including but not limited to algorithms, models, pipelines, training, inference, testing, performance tuning, deployments
• Review MVP implementations, provide recommendations and ensure ML best practices and guidelines are followed
• Act as ‘Owner’ of end-to-end machine learning systems and their scaling
• Translate machine learning algorithms into production-level code with distributed training, custom containers and optimal model serving
• Industrialize end-to-end MLOps life cycle management activities including model registry, pipelines, experiments, feature store, CI-CD-CT-CE with Kubeflow/TFX
• Accountable for creating, monitoring drifts leveraging continuous evaluation tools and optimizing performance and overall costs
• Evaluate, establish guidelines, and lead transformation with emerging technologies and practices for Data Science, ML, MLOps, Data Ops