Nizamudheen T
Data Scientist @Prevalent AI
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WORK HISTORY
Data Scientist @Prevalent AI
Kochi, IN
Gen AI Agentic Workflows: Led the design and implementation of advanced agentic workflows leveraging large language models to interact with knowledge graphs and generate deep cybersecurity insights. Developed and optimized generative UIs to enhance user engagement, facilitate intuitive data exploration, and improve decision-making in cybersecurity applications. Conversational AI: Engineered and fine-tuned sophisticated Conversational AI systems within the cybersecurity domain, leveraging large language models and advanced natural language processing techniques. Implemented Retrieve and Generate (RAG) methodologies to enhance response accuracy and contextual relevance. Designed AI agent workflows for advanced graph query generation and data inferencing, enabling intelligent and automated cybersecurity solutions. Data Quality & Profiling Solution: Designed, developed, and owned an enterprise-level data quality assessment framework utilizing advanced data science methodologies and Generative AI agents. Applied statistical analysis, anomaly detection, and probabilistic modeling to assess and improve data integrity, ensuring high accuracy and reliability in customer and operational datasets. Delivered actionable insights that supported data-driven strategies across the organization. Synthetic Data Generation: Developed and implemented an advanced agent-based synthetic data generation framework tailored for complex cybersecurity datasets. Leveraged probabilistic modeling, deep learning, and generative adversarial networks (GANs) to create high-fidelity synthetic datasets, enhancing the availability of high-quality training data for machine learning models. This innovation strengthened model robustness and improved cybersecurity threat detection capabilities.
EDUCATION
NATIONAL INSTITUTE OF ELECTRONICS & INFORMATION TECHNOLOGY (NIELIT)
Advanced Diploma In Artifitial Intelligence, Data science and machine Learning
L.B.S College of Engineering , Kasaragod
Bachelor of Technology - BTech, Computer Science And Engineering
College of Engineering Trivandrum
Master of Technology - MTech, Artificial Intelligence
ABOUT NIZAMUDHEEN T
I’m a Data Scientist at Prevalent AI, working at the intersection of Data Science, Generative AI, and cybersecurity to design and operationalize intelligent, production-grade security systems. My focus is on building scalable AI architectures that combine statistical analysis, machine learning, and LLM-powered reasoning to deliver reliable and actionable security insights.From a data science perspective, I work on problems such as data quality measurement, anomaly detection, synthetic data generation, feature engineering, and analytical modeling across large, heterogeneous cybersecurity datasets. I’ve designed data quality frameworks that quantify how data integrity impacts analytical reliability, helping security teams reason about confidence, risk, and decision accuracy using measurable signals.On the Generative AI side, I design LLM- and RAG-based systems that reason over cybersecurity knowledge graphs to enable context-aware analytics, vulnerability intelligence, and proactive risk detection. I’ve led initiatives to establish Agentic Data Platform architectures, leveraging remote AI agents and the Model Context Protocol (MCP) to standardize data onboarding, automate insight generation, and accelerate both internal and client workflows.I operate at the intersection of architecture, execution, and technical leadership, guiding decisions around LLMOps strategy, model selection, system trade-offs, and cost optimization. My hands-on experience includes fine-tuning Small Language Models (SLMs) for domain-specific use cases such as asset classification, software extraction, and vulnerability consequence prediction, applying PEFT, model quantization, and prompt optimization to achieve accuracy, efficiency, and scalability in real-world deployments.Beyond core modeling, I contribute to Generative UI and Conversational AI design, enabling intuitive human–AI interaction for complex security data while emphasizing explainability, trust, and responsible AI deployment.With a Master’s degree in Artificial Intelligence, I’m driven by system-level thinking and technical leadership, with a long-term focus on building adaptive, data-centric, and agentic AI systems that make cybersecurity operations more intelligent, resilient, and decision-focused.
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