AI Research Engineer with less than a year in Machine Learning & AI Models
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Data Science Intern with 11 months of experience in Machine Learning Modeling and Research. Proven ability to develop and maintain ML pipelines for rainfall prediction, contribute to climate risk assessment, and analyze climate data for environmental sustainability. Skilled in applying machine learning techniques to derive actionable insights from ecological and climate data, and has developed multiple AI/ML projects including a RAG-based AI Copilot and a CNN-based plant disease classification system.
Indian Institute of Information Technology Lucknow
M.Sc · Data Science
August 1, 2025 – June 30, 2027
University of Lucknow
B.Sc · Statistics
August 1, 2022 – June 30, 2025
Darukaa.earth
Climate Data Science Intern
March 1, 2026 – Present
India
Climate Resilience Observatory (CRO), U.P. Govt.
Data Science Intern
September 1, 2025 – March 1, 2026
India
Rainfall Prediction Model
June 25, 2026 – Present
Built an end-to-end ML pipeline to model rainfall using datasets from NASA POWER and IMD. Performed in-depth climate data analysis to support farmer advisory and agricultural planning.
View ProjectRAG / Agent Based AI Copilot
June 25, 2026 – Present
Built a multi-agent AI assistant for supply chain managers to analyze shipment data, compute delays, and answer operational queries using natural language. Integrated HuggingFace LLM with FAISS vector store for semantic retrieval over dynamic CSV uploads.
View ProjectPlant Disease Classification system
June 25, 2026 – Present
Developed a CNN-based DL model for plant disease classification, achieving 95% accuracy across 3 disease categories. Built and deployed a FastAPI backend for real-time image inference and HTML, CSS, JS for image upload and diagnosis visualization. Deployed the end-to-end application on Google Cloud Platform (GCP).
View ProjectCultural Fit Analysis
The candidate's projects and internships show a strong interest in AI/ML applications, particularly in environmental and agricultural domains. This specialization, while valuable, might indicate a narrower scope of experience compared to a broader AI research role. The focus on personal projects and academic internships suggests a proactive learning approach. However, the lack of diverse industry experience or team-based project descriptions makes it challenging to fully assess cultural fit beyond technical alignment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems from data sourcing to deployment. The internship experiences suggest a capacity for research and practical application of data science techniques. However, without specific assessment data on soft skills, it's difficult to fully evaluate collaboration, problem-solving under pressure, or communication in a team setting.