Pratham Kuril

AI Engineer

G3NR8

Aug 2025 - Current (1 year 3 months)

• Developed a RAG system with hybrid search for a leading global engineering consultancy with 18,000+ employees, achieving 85% accuracy in content retrieval and enabling intelligent Q&A across 1000+ technical documentation and knowledge bases
• Built an AI-powered content repurposing platform for a €1.4B technology services provider, automatically transforming engineering reports and technical content into 10+ campaign ready digital marketing assets , reducing manual content creation effort by 70% for their global marketing teams
• Conducted AI discovery workshops and feasibility assessments for enterprise clients, identifying high-impact use cases and designing tailored AI roadmaps that resulted in 60% faster AI adoption across c

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Founding AI Engineer

BRAID TECHNOLOGIES

Jan 2025 - Mar 2025 (3 months)

• Architected a comprehensive engineering toolkit that generates C4 diagrams for system visualization, converts code repositories to LLM-optimized text chunks, and automatically generates test code using the OpenAI Assistant API or Google Gemini 1.5-pro, increasing test coverage to over 90% while reducing development time by 25%
• Implemented structured prompt lifecycle management system, cutting maintenance by 40% and improving response accuracy by 25% through externalized prompt templates and automated quality evaluation
• Led technical standardization across enterprise pattern applications, implementing shared APIs, consistent data models, and reusable components that improved code quality by 35% and reduced integration issues

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Generative AI Technical Intern

BRAID TECHNOLOGIES

Jul 2024 - Dec 2024 (6 months)

• Engineered Azure-hosted AI chat assistant for a leading British telecom (500M+ customers) using GPT-4o and Gemini-1.5-pro, enabling the upskilling of 24 professionals into AI developers
• Developed a complete text analysis system, including both a Typescript Edge plugin for real-time web page classification and a Python backend pipeline for text summarization and cluster analysis using Azure-hosted LLMs
• Improved LLM evaluation accuracy by 30% for an AI-enabled learning assistant by developing a comprehensive testing framework that implements persona-based strategies, cross-model validation using Azure OpenAI and Google Gemini, and vector embedding similarity calculations with text-embedding-3-large Show less

Program Representative - MSc Data Science

City, University of London

Sep 2023 - Dec 2024 (1 year 4 months)

• Served as the Program Representative for the MSc Data Science cohort of over 100 students (2023-2024)
• Organized and coordinated the annual Christmas party for the School of Science and Technology, fostering a sense of community among students and faculty.
• Attended Student-Staff Liaison Committee (SSLC) meetings, actively representing the voice and concerns of the Data Science student body
• Facilitated effective communication between students and the university administration, addressing academic issues and providing constructive feedback Show less

Machine Learning Programmer Trainee

Bitwise

Jul 2022 - Jun 2023 (1 year)

• Engineered a Python tool with a command line interface to analyze and identify similarities across ETL workflows in XML files generated by Informatica across an entire directory, resulting in a linked Excel sheet with workflows classified into reusable structures, visualized workflows for each identified pattern of workflow ,similarity findings and metrics for each pattern, enhancing efficiency in ETL development tasks.
• Spearheaded the development process, creating a similarity logic using point-based systems, Jaccard similarity, and Levenshtein distance, and generating a diagrammatic representation of workflows, improving user interaction and clarity of results through integrated Excel outputs and terminal deployment.
• Deliv

AI/ML Intern

Prescient Technologies

Mar 2021 - Jun 2021 (4 months)

• Developed a custom object detector trained using a custom dataset.
• Computed solutions on computer vision problems in the Machine Learning domain.
• Reviewed and examined the performance of 6 Artificial Neural Networks while computing solutions with the use of transfer learning for the given problem statement.
• Documented and summarized the findings while working on the project.
• Presented the project and added suggestions with respect to future scope.

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Pratham Kuril

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