Mohammed Afsal B

Architected multi-agent LLM workflows (LangGraph + Azure GPT-4) that automated end-to-end IT ticket resolution across Salesforce, HubSpot, and Zoho, reducing manual agent intervention by 60%+ and handling 1,000s of monthly service requests autonomously — deployed across 5+ enterprise clients. Built end-to-end AI automation pipelines (NLP + LLM + RPA) that eliminated recurring manual workflows across multiple client environments — directly reducing operational overhead and freeing teams from high-volume, low-value processing tasks. Designed and optimized RAG pipelines over large-scale enterprise knowledge bases, grounding LLM outputs in proprietary data and measurably improving factual accuracy and stakeholder trust in AI-generated responses. Collaborated across 4-country delivery teams (USA, Australia, India, Japan) to scope, prototype, and ship AI products under tight client deadlines — owning the full cycle from architecture design to production deployment.

› Processed and analysed large-scale genomic datasets using R and SnapGene, applying statistical models and sequence alignment pipelines — findings published in Communications Biology (Nature Portfolio). › Built reproducible, well-documented data analysis workflows, developing the engineering rigour that directly translates to production-quality ML pipelines. › Applied bioinformatics methods to high-dimensional biological data, bridging domain science and data engineering in a peer-reviewed research context.

Relevant Courses: BIOLOGICAL DATA ANALYSIS INTRODUCTION TO ALGEBRA SINGLE VARIABLE CALCULUS MULTIVARIABLE CALCULUS INTRODUCTION TO PROBABILITY AND STATISTICS