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AI Engineer with less than a year in Data Analysis & LLM Applications
Results-driven Data Analyst and AI/ML Developer with hands-on experience building end-to-end data pipelines, RAG-based LLM applications, predictive models, and interactive BI dashboards using Python and SQL. Proven track record of transforming complex public datasets into actionable business insights through statistical modelling, regression analysis, and clustering. Proficient in modern AI tooling (LangChain, Groq, Ollama) alongside core analytics competencies. Skilled at stakeholder communication, data storytelling, and KPI reporting to drive strategic decision-making.
Boston Institute of Analytics
Dual Certification -Data Science and AI · Data Science and AI
N/A – June 30, 2026
Benedictine Academy
Bachelor of Computer Applications (BCA) · Computer Applications
N/A – June 30, 2024
G.V.H.S School
Higher Secondary · Science
N/A – May 31, 2021
WECARE EDUTECH
Education Consultant
May 1, 2025 – November 1, 2025
India
KTM PMNA
Pro Bike Advisor
February 1, 2025 – April 1, 2025
India
Miracle Graphics Institute
Graphic Design Intern
January 1, 2023 – March 31, 2023
Malappuram, Kerala, India
Multi-Document Legal / Medical RAG Analyzer
June 30, 2026 – Present
Built a production-style RAG pipeline capable of ingesting multiple legal and medical PDF documents, chunking and embedding content into a vector store, and answering domain-specific queries with source-grounded responses. Implemented retrieval logic to rank and surface the most contextually relevant document chunks per query, minimizing hallucination and improving answer accuracy. Demonstrates applied knowledge of modern LLM architecture patterns (RAG), directly relevant to enterprise AI and data engineering roles.
Financial Inclusion State-Level DBT Predictive Model
June 30, 2026 – Present
Analyzed historical Direct Benefit Transfer (DBT) records across 36 Indian states using a Random Forest Regressor to accurately forecast total transfer amounts based on state-level features. Applied end-to-end ML workflow: data cleaning, feature engineering, model training, hyperparameter tuning, and evaluation — achieving interpretable predictions with visualized feature importances. Produced stakeholder-ready performance scorecards enabling data-driven prioritization of digital infrastructure investments across at-risk states.
AI Resume Screener
June 30, 2026 – Present
Engineered an automated resume screening system that parses candidate CVs, extracts key skills and experience signals, and ranks applicants against a job description using semantic similarity. Combined NLP-based feature extraction with retrieval techniques to surface the most relevant profiles — reducing manual screening effort and improving shortlisting precision. End-to-end project covering data ingestion, text preprocessing, model inference, and ranked output generation.
Accident Data Analytics & Predictive Modelling Dashboard
June 30, 2026 – Present
Transformed raw public-sector road accident datasets into a predictive ML model using Linear Regression to forecast future fatality counts with visualized trend lines. Conducted comprehensive EDA, feature encoding, and data cleaning; delivered an interactive dashboard enabling non-technical stakeholders to explore insights and safety protocol recommendations.
Profit Pilot - Business Analytics API
June 30, 2026 – Present
Developed a Flask-based analytics backend exposing REST API endpoints delivering real-time KPIs: revenue trends, customer behaviour metrics, and sales performance dashboards from local data sources. Architected clean data pipelines from ingestion through transformation to API delivery — mirroring production BI engineering workflows.
Cultural Fit Analysis
The candidate's project portfolio shows a strong interest and practical application in AI/ML, data analysis, and RAG systems, aligning well with an AI Engineer role. The diversity of projects, from legal/medical RAG to financial predictive models and resume screening, indicates a broad curiosity and ability to apply AI/ML across different domains. However, the professional experience is not directly in software engineering or AI/ML, which might require a cultural adjustment to a more technical, product-focused environment.
Soft Skills & Operational Fit
The candidate demonstrates soft skills such as data storytelling, stakeholder presentations, critical thinking, and cross-functional collaboration, which are valuable for an AI Engineer role that often requires communicating complex technical concepts to non-technical stakeholders. Their experience in consultative roles suggests an ability to understand user needs and align technical solutions accordingly.