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AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
LLMDriftExperiment
March 29, 2026 – Present
A high-fidelity research platform for quantifying LLM Drift: the phenomenon where large language models deviate from their established personas, reasoning standards, and emotional baselines during prolonged, adversarial multi-agent interactions.
View ProjectPyCaretAgent
January 7, 2026 – Present
PyCaretAgent is an autonomous AI agent framework that extends PyCaret with advanced reasoning and tool-use capabilities using the Google Generative AI SDK (google-adk). It bridges the gap between natural language requirements and production-ready machine learning pipelines.
View ProjectCognitoEDA
July 18, 2025 – Present
CognitoEDA is an agentic workflow for automated Exploratory Data Analysis (EDA) using Large Language Models (LLMs) and Pandas. The project leverages LangChain, LangGraph, and Google Gemini models to extract metadata, generate EDA queries, and produce human-readable structured reports from tabular data.
View ProjectIntellect-Hire-App
February 11, 2024 – August 17, 2025
"Intellect Hire: The AI Talent Curator" streamlines hiring by extracting resume details, prompting users to upload resumes, matching candidates with jobs, and evaluating candidate suitability, enhancing recruitment efficiency and accuracy.
View ProjectAI-MLOps-TimeSeries
January 12, 2022 – Present
A self-hosted MLOps platform for time-series forecasting. Upload a CSV of one or more series, clean it (outlier detection + imputation), run a battery of forecasting models with cross-validation, and explore the forecasts and accuracy metrics in an interactive dashboard.
View ProjectDocker-Model-Training
October 20, 2021 – November 18, 2021
Docker-Model-Training — GitHub repository
View ProjectCultural Fit Analysis
The candidate exhibits a strong cultural fit for a role requiring innovation, self-direction, and a passion for advanced AI/ML technologies. The diverse range of personal projects, from LLM agents to MLOps platforms, demonstrates a broad interest and hands-on experience in key areas relevant to a Data Scientist role, especially one with an AI/ML focus. The emphasis on open-source tools and frameworks (PyCaret, LangChain, Google Generative AI SDK) aligns with a collaborative and modern development culture. However, the lack of team-based or professional experience projects makes it harder to assess collaboration in a corporate setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and innovative approach to problem-solving, particularly in the AI/ML domain. The focus on autonomous agents, LLM drift, and MLOps suggests a strong inclination towards cutting-edge research and practical deployment challenges. However, without specific psychometric or English test results, it is difficult to assess communication clarity, teamwork, or stress handling capabilities directly. The detailed project descriptions suggest good technical communication skills.