# Srijeet Chatterjee > Software Engineer, Machine Learning @ WhatsApp | Generative AI, LLM Location: London Area, United Kingdom, United Kingdom Profile: https://flows.cv/srijeet At WhatsApp, contribute to Integrity initiatives as a Software Engineer specializing in Machine Learning, Generative AI, and Large Language Models (LLMs). Bring advanced expertise in prompt tuning and other AI techniques to enhance systems and processes. With over four years of experience in machine learning, applied research, and AI system design, previously supported projects at UBS and Deloitte. Committed to leveraging emerging AI technologies to develop innovative solutions that address real-world challenges. ## Work Experience ### Software Engineer, Machine Learning @ WhatsApp Jan 2025 – Present | London Area, United Kingdom • Designed and launched the first neural-network-based enforcement classifier on WhatsApp (2.5B+ MAU), targeting scam actors and reducing scammer-engaged conversations by 18%. • Independently identified and pursued the initiative to curb Victim-Initiated Scams—contributing to >50% of all scams on the platform—by researching, prototyping, and validating enforcement models across two new operational contexts (disconnect events and wa.me entry points). • Owned the full initiative lifecycle end-to-end: conducted impact analysis, trained classifiers operating on 300M+ disconnect events and 40M+ wa.me events daily, designed and executed 3-week A/B experiments, led experiment result evaluation with the team, and launched to production—delivering an 11% reduction in the SEC topline metric, eliminating ~3M Scam Engaged Conversations on a 7-day rolling average. • Collaborated with the SMB (Small & Medium Business) team to extend enforcement models to business accounts, broadening the integrity coverage surface across WhatsApp. ### Data Scientist, Director @ UBS Jan 2022 – Jan 2025 | London, England, United Kingdom Working on advanced AI Agent systems leveraging Large Language Models (LLMs) and multimodal inputs. Focused on applied research and ML system design. ### Senior Machine Learning Engineer @ Deloitte Jan 2022 – Jan 2022 | London, England, United Kingdom ### Data Scientist - Artificial Intelligence @ IBM Jan 2019 – Jan 2022 | Bengaluru Area, India -- Developed an automated problem-solution extractor and multi level ranking algorithm using POS tagging, SVD based QoS score, domain specific patterns and keywords. The solution now acts as a one stop solution for the client vendors to access multiple tools, get virtual SME support, new onboard's virtual guide and build on the knowledge repository. -- Designed deep learning based encoder-decoder architecture for the solution generator block with ReLU boosted LSTM as building blocks, trained on historical tickets and deployed as a web API using Flask. -- Worked on document search API development for the design documents such as HLD,LLD, acceptance criteria docs, wikis and Ranking using Tika and tf-idf matching that improved the Defect Retest Efficiency by 17 percent. -- Developed the feedback system for the predicted solutions with cloudant db. ### Teaching Assistant - Machine Learning @ Indian Institute of Technology, Delhi Jan 2018 – Jan 2018 | Delhi Area, India -- Teaching Assistantship, Machine Intelligence and Learning, (July 2018 - Dec 2018) where jointly conducted tutorial sessions on Optimisation Problems, SVMs, CNNs, RNNs and GANs for the under-grads of IITD. ### Machine Learning Engineer - Intern @ IBM Jan 2018 – Jan 2018 | Bangalore -- Design of Recommender Engine for IBM Services Platform with Watson : Adopting Artificial Intelligence in making intelligent decision with respect to mapping consumable services with respective account on Platform, submitted Patent search -2 for the approach, IBM Global Technical Solutions Lab, Bangalore/ISPW Team, May 2018 - July 2018 ### Data Scientist - Advanced Analytics @ Tata Consultancy Services Jan 2013 – Jan 2017 | Kolkata Area, India -- This role involved understanding the business problem and solving the same using different analytical techniques, especially in the Credit Card LifeCycle Management. Examples include – Promotional Effectiveness, Sales Team SLA Count Analysis etc. Explicit usage of several techniques to leverage the hierarchical structure in data including, but not limited to Hierarchical Bayesian and Hierarchical Linear Modelling. -- Used predictive modelling, statistics, Machine Learning, Data Mining, and other data analysis techniques to collect, explore, and extract insights from structure and unstructured data. -- Developed software, algorithms and applications to apply mathematics to data, performed large scale experimentation and built data driven apps to translate data into intelligence, solve a variety of business problems and enable business strategy. ## Education ### Master of Technology - MTech in Computer Technology / Machine Learning Indian Institute of Technology, Delhi ### Bachelor of Technology (B.Tech.) in Electronics and Communications Engineering Techno India College Of Technology ### 12th in Maths and Science Kendriya Vidyalaya ## Contact & Social - LinkedIn: https://linkedin.com/in/srijeet-chatterjee-a85b0338 - Website: https://medium.com/@srijeetchatterjee - Website: https://topmate.io/chatterjee_srijeet/ --- Source: https://flows.cv/srijeet JSON Resume: https://flows.cv/srijeet/resume.json Last updated: 2026-04-05