AI Engineer with less than a year in Machine Learning and Generative AI
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Assessing your cultural and operational fit
Currently pursuing a B.Tech in IT, I am an enthusiastic AI Intern with hands-on experience in Machine Learning and Generative AI. I have successfully developed a domain-specific AI chatbot using RAG, LangChain, and LLM APIs, and built an FWI Predictor achieving 87% accuracy in fire risk prediction. My expertise spans Python, data preprocessing, and creating adaptive AI workflows with multi-modal QA pipelines, validated for high accuracy and reduced latency.
KIET Group Of Institutions
BTech in IT · Information Technology
August 1, 2023 – June 30, 2027
st. Joseph's School
Intermediate
June 1, 2021 – May 31, 2022
HOOC AI Technologies
AI Intern
February 1, 2026 – Present
India
BUDDY
June 1, 2025 – June 1, 2026
Built a domain specific AI chatbot for KIET capable of answering 50+ student queries using Retrieval-Augmented Generation. Implemented embedding based semantic retrieval over university policy and academic documents for accurate context fetching.
View ProjectQuint AI
March 1, 2025 – June 1, 2026
Developed a multi-model QA pipeline integrating RAG + external tools + LLM orchestration.. Achieved 95 percent response accuracy as validated by human evaluators, reducing retrieval latency by 40 percent.. Implemented LangChain and LangGraph to design agentic AI workflows, enabling seamless tool coordination and adaptive reasoning.
View ProjectFWI PREDICTOR
October 1, 2024 – June 1, 2026
Developed a supervised Machine Learning system (Random Forest, Logistic Regression) achieving 87 percent accuracy in predicting fire risk. Processed 50,000+ environmental records (temperature, humidity, wind speed, rainfall, fuel moisture) for feature engineering and model training. Optimized data preprocessing pipeline, reducing computation time by 30 percent and improving model precision by 22 percent on seismic datasets. It was a team project with 2 members including me.We took approx 1 month for building this.
View ProjectAWS AI practitioner
AWS, Accenture, Udacity
June 1, 2026 – Present
Winner - Brainwave 2.0 (DTU)
DTU
June 1, 2026 – Present
British Airways – Data Science
British Airways
June 1, 2026 – Present
IEEE Hackathon
IEEE
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects and internship show a strong interest and practical application in AI, aligning well with an AI Engineer role. Participation in hackathons and certifications like 'AWS AI practitioner' and 'British Airways – Data Science' demonstrate initiative and a commitment to continuous learning, which are positive indicators for cultural fit in a dynamic tech environment. The diversity of projects (chatbot, fire prediction, multi-model QA, emotional analysis) suggests adaptability and a broad interest in AI applications.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project work and hackathon participation. Their ability to work in a team (FWI PREDICTOR project) and manage project timelines (1 month for FWI PREDICTOR) suggests good operational fit. The descriptions of optimizing pipelines and achieving specific accuracy/latency improvements indicate a results-oriented approach. However, without direct interview data, communication and collaboration soft skills are inferred primarily from project descriptions.