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AI Engineer with less than a year in Machine Learning & LLM APIs
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Assessing your cultural and operational fit
Computer Science graduate who enjoys building AI tools that solve real problems. Over the past year, I have built and deployed two full projects—a data quality platform and a document intelligence system—using Python, LLM APIs, and machine learning. I learn fast, I like working on things that actually get used, and I am looking for a role where I can keep building and growing as an AI Engineer or Software Developer.
Anil Neerukonda Institute of Technology & Sciences
Master of Technology · Computer Science
August 1, 2025 – Present
Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology
Bachelor of Technology · Computer Science
August 1, 2021 – June 30, 2025
Narayana Junior College
Intermediate Education
June 1, 2019 – May 31, 2021
Narayana e-Techno School
10th Standard Education
June 1, 2018 – May 31, 2019
EduSkills × AICTE — Supported by AWS Academy
AI-ML Virtual Intern
December 1, 2022 – February 1, 2023
India
Insight PDF Pro - Document Intelligence System
June 1, 2025 – June 1, 2026
Built and deployed a system where users can upload several PDFs at once and simply ask questions about them - instead of reading through every page manually. Wrote a pipeline that breaks each PDF into smaller chunks and tags them, so the system knows exactly which part of which document to look at before answering a question. Used n8n to handle everything behind the scenes – uploading files, finding relevant chunks, and generating summaries – without needing any manual steps in between. Made sure every answer shows which document and section it came from, so users can trust what they are reading and verify it themselves. Built it to handle multiple documents at once and remember the conversation, so users can keep asking follow-up questions without losing context.
Verifiq - Intelligent Data Quality Platform
June 1, 2025 – June 1, 2026
Built and deployed a tool that scans any uploaded dataset and tells you exactly what is wrong with it — missing values, duplicates, bad formats, outliers — before you waste time running analysis on bad data. Created a scoring system that gives your dataset a quality score from 0 to 100, so you immediately know how clean your data is without digging through every column yourself. Plugged in Groq LLM so that for every error the tool finds, it also tells you in plain English what to do about it — no need to figure it out yourself. Added a one-click fix button that automatically fills in missing values, removes duplicate rows, fixes negative numbers, and gives you a clean file to download — the whole process takes seconds. Built a schema checker where you tell the tool what each column should look like, and it immediately flags anything that does not match — useful when data comes in from different sources or systems. Used IsolationForest from Scikit-learn alongside Z-Score analysis to catch rows that look statistically unusual — the kind of values that are easy to miss but can quietly break a model or analysis. Built a dashboard with Plotly that shows you completeness bars, a heatmap of where your missing values are, error breakdowns by type, and value distributions — so you can see the full picture of your data in one place.
Prompt Engineering for ChatGPT
Unknown
June 1, 2026 – Present
Machine Learning Fundamentals
Alteryx
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in applying AI to solve real-world problems, aligning well with an innovative and impact-driven culture. The breadth of skills across AI/ML, data processing, and deployment tools suggests adaptability. The pursuit of a Master's degree while also completing significant personal projects indicates a commitment to continuous learning and growth. The target role of 'AI Engineer' aligns perfectly with the demonstrated project work.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving mindset and an ability to translate complex technical concepts into practical applications. The focus on user-centric features (e.g., verifiable answers, one-click fixes) suggests an understanding of product utility. The self-driven nature of personal projects points to initiative and a desire to build impactful tools. However, without direct interview data, assessing collaboration, stress handling, and communication in a team setting is not possible.