Mohammad Atir

Background

About Mohammad Atir

Mohd Atir is a Manager with Deloitte's Cloud Engineering practice. He brings with him 13+ years of developing and deploying AI and machine learning cloud solutions. He has previously led multiple multi-million Dollar transformation programs at a Middle East retail Bank, and more recently the development of a European Cloud Strategy for a Global Bank. He also brings experience having worked on a platform migration/ separation at one of India’s largest retail banks- Worked on 20+ Data Engineering projects leveraging Big Query ML, Cloud Run, GCS Buckets, PubSub, Dataflow, Vertex AI, Looker, AWS RedShift, S3, Data Sync, Sagemaker, Azure Synapse, Data factory, and SAS Viya, and applied cutting edge algorithms to drive data driven decisions- Oversaw and managed £3m+ UPI payment automation and migration program, successfully delivering benefits in line with Business Case, for an Indian Retail Bank- Managed the scope and governance of monthly code releases, inc. release plans and updates to stakeholders across the business - Developed the AI model with ML algorithm to predict the Net Promotor Score for the Relationship Managers, which can help in Risk Mitigation through early identification of low scoring RMs - Led the transformation of Collection strategy, across all four LOBs of the largest ratings organization, worth US$ 6 billion impacting US$ 0.5 billion written-off invoices. AI model was developed and deployed on cloud platform with access to the CFO Office Executives- Led the Model development to predict Claims category based on its features and customer information. Applied Extra trees classifier technique in Python to identify claims category. Led AI workflow design deployment on Google Cloud Platform for a US Insurance client- Led the team to develop a solution to provide a projection of net losses for the wholesale banking portfolio by month. The model output was used as a Quantitative tool to support business unit of a Largest North American Bank using PD, EAD, and LGD model in the background with Stress testing at CECL and DFAST standards.z - Developed Demand Forecasting model for the world’s largest oil producer in Saudi Arabia for its Oil Supply Planning Business Unit. This model was developed for its product portfolio at its bulk plant level for its daily, weekly, and monthly demand- Supported delivery on multiple workstreams for a Platform migration for one of Middle east’s largest retail banks, and managed the scope and governance of monthly code releases, inc. release plans.