AI Engineer with less than a year in Generative AI, LLMs & MLOps.
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Assessing your cultural and operational fit
Data Science Intern proficient in developing Generative AI applications using Python, Large Language Models (LLMs), CrewAI, and Ollama. Experienced in building multi-agent AI systems for task planning, reasoning, and report generation, and deploying end-to-end AI solutions on cloud platforms. Skilled in prompt engineering, model evaluation, and Retrieval-Augmented Generation (RAG) to enhance AI response quality and user experience. Also skilled in Python, SQL, Power BI, Tableau, Streamlit, Flask, WordPress, Microsoft Excel, Scikit-learn, Machine Learning, and various data analysis and visualization techniques.
Mahendra Institute of Technology
B.E · Computer Science and Engineering
August 1, 2022 – June 30, 2026
Besant Technologies
Data Science Intern
January 1, 2026 – Present
Bengaluru, Karnataka, India
Multi-Agent Healthcare Assistant
June 23, 2026 – Present
Developed a multi-agent AI system using CrewAI and Ollama to analyze blood test reports, conduct medical research, and generate personalized health recommendations automatically. Implemented Retrieval-Augmented Generation (RAG) using web search and FAISS vector databases to improve response accuracy through real-time and local medical knowledge retrieval. Orchestrated a four-agent workflow comprising Blood Report Analyzer, Medical Researcher, Health Advisor, and Report Writer to generate structured healthcare reports with evidence-backed recommendations.
Stock Trading Simulator
June 23, 2026 – Present
Built a multi-agent stock trading simulator powered by local LLMs (DeepSeek-R1 via Ollama), featuring Market Maker, Trend Follower, and Arbitrage trading agents. Designed a RAG-based financial intelligence system using FAISS and Ollama embeddings to provide agents with context-aware market news and sentiment analysis for trading decisions. Developed a realistic trading environment with order-book matching, portfolio tracking, PnL analytics, Sharpe Ratio calculations, and interactive dashboard visualization for strategy evaluation.
Cultural Fit Analysis
The candidate's projects demonstrate a proactive and innovative approach to problem-solving, which aligns well with a dynamic AI engineering environment. The diversity of projects (healthcare, finance) and the use of various tools (CrewAI, Ollama, Streamlit, Flask) suggest adaptability and a willingness to explore different technologies. The internship experience, though brief, indicates an interest in practical application. However, the candidate is still pursuing a bachelor's degree, which might imply a need for mentorship and structured guidance in a professional setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to conceptualize, design, and implement complex AI systems, suggesting strong problem-solving and execution skills. The multi-agent approach in projects implies an understanding of system orchestration and modular design. However, without direct assessment data, specific soft skills like teamwork, leadership, or stress handling cannot be definitively evaluated.