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AI Research Engineer with less than a year in Machine Learning & NLP
Highly motivated and results-oriented professional with 0.5 years of experience as a Machine Learning Intern, specializing in Python, Scikit-learn, XGBoost, Pandas, SHAP, and Matplotlib. Proven ability to build end-to-end Machine Learning pipelines, design data preprocessing workflows, implement ensemble models achieving high accuracy, and evaluate model performance using advanced metrics. Actively involved in developing multi-agent AI systems, college inquiry chatbots, and agentic clinical AI systems (MediAgent-RAG) leveraging LLMs and advanced NLP techniques. Eager to contribute to innovative AI solutions.
M.S.Ramaiah university of applied science
M.Tech · AI/ML and Data Science
August 1, 2024 – June 30, 2026
Erode Sengunthar Engineering college
B.Tech · Computer Science and Engineering
August 1, 2020 – June 30, 2024
Bangalore, India
Machine Learning Intern
January 1, 2026 – Present
Bengaluru, Karnataka, India
Multi-Agent AI Research System
June 24, 2026 – Present
Architected a multi-agent AI system leveraging LangGraph to enable task decomposition, coordination, and parallel execution across specialized agents (Research, Summarization, Validation). Integrated LLM-driven reasoning using GPT models for context-aware response generation, enhancing answer relevance and coherence. Developed asynchronous agent execution and orchestration, reducing overall response latency by ~30% for complex multi-step queries. Developed agent memory and context-sharing mechanisms to maintain conversation state, improving multi-turn query accuracy by ~20%.
View ProjectMediAgent-RAG: Agentic Clinical AI System
June 24, 2026 – Present
Architected an agentic RAG system, converting dense clinical PDFs into structured discharge summaries and enhancing intent understanding accuracy to approximately 90%. Implemented robust failure handling (retry + fallback) and observability (step-level traces) for reliable, explainable AI decision-making. Defined an intelligent agent loop with sophisticated tool routing, guaranteeing safe, no-fabrication outputs and optimizing medication reconciliation accuracy to 100%. Transformed feedback-driven learning loop combining edit-distance reward and memory, boosting model accuracy by 38% and cutting repeated errors across ten iterations
College Inquiry AI Chatbot System
October 1, 2025 – Present
Improved an AI-powered college inquiry chatbot, automating admissions, fees, placements, and academic queries, enhancing answer relevance by 30%. Implemented retrieval-based response system using TF-IDF vectorization and cosine similarity, improving answer accuracy and relevance by ~30% over baseline keyword matching. Integrated transformer-based generation using Flan-T5 to produce context-aware responses for complex and unseen queries. Developed advanced NLP preprocessing modules, enhancing model input quality through tokenization and normalization, increasing intent understanding accuracy to approximately 88%.
View ProjectCultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and focus on advanced AI/ML topics, particularly in NLP and agentic systems, which aligns well with an AI Research Engineer role. The diversity of projects (chatbot, clinical AI, multi-agent research) shows a broad application of AI concepts. The candidate is currently pursuing a Master's in AI/ML and Data Science, indicating a commitment to continuous learning and staying updated with the field. The experience, though primarily academic and internship-based, shows initiative and a drive to apply theoretical knowledge to practical problems. The psychometric test score of 346/500 suggests moderate alignment in areas like logical reasoning and work attitude, but further assessment would be beneficial.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems, suggesting strong problem-solving skills. The focus on metrics and improvements (e.g., accuracy, latency reduction) points to a results-oriented approach. The mention of failure handling and observability in the MediAgent-RAG project suggests an understanding of robust system design, which is crucial for operational fit in an engineering role. However, without direct interview data, specific soft skills like teamwork or stress handling cannot be fully assessed.