Roles and responsibilities : Part of Data Science team to develop high impact, scalable and sustainable ML solutions Collaborate with Senior Data Scientists and product managers to understand customer needs and co-own the deployment and maintenance of key ML models Work closely with Data Scientists on text and image data using the leading ML techniques in GenAI/OCR/image processing Develop end to end pipeline and further enhancement of solutions for scale and stability Build quality checks, unit tests and diagnostic reports for code and output quality monitoring using python, SQL and yaml Conduct modeling experiments and report automation Scheduling, automating and stabilizing the current AWS pipeline using jenkins, Step Function, Lambda, Runtime, Sagemaker Develop in-house capability to deploy custom models on Sagemaker endpoints, taking care of platform and library compatibility Explore GCP capabilities to deploy projects - Code Versioning, end points, GCS, Bigquery and automation/scheduling Preferred Qualifications :  Curiosity to learn Advanced ML, Data Science + ML Ops  2-4  years working experience as a ML Engineer Hands-on experience with Python and SQL is must Understanding of data modeling, data access, data storage, and optimization techniques Experience working with cloud-based technologies and development processes An ideal candidate would be expected to have basic familiarity of GCP and AWS infrastructure. Advanced knowledge of MLOps-related tools like Sagemaker, ECR, Step Functions, Vertex AI is preferred Ability to quickly understand the tech pipeline/project infrastructure and engage deeply with Team members. Delivery quickly on new development or optimization related action items Quick action oriented approach preferred over brainstorming on very long term ideas