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AI Automation Engineer with less than a year in Python & Deep Learning
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring AI & Automation Engineer with a strong technical foundation in Python, Deep Learning, and SQL. Passionate about building next-generation applications using LangChain and RAG (Retrieval-Augmented Generation) architectures. Uniquely skilled in workflow automation using n8n to bridge the gap between AI models and real-world processes. Eager to launch a career where I can apply my skills in Generative AI and data analysis to solve complex business problems.
JB Institute of Engineering and Technology
Masters in Business Administration · Finance
January 1, 2020 – January 1, 2022
Bhaskar Engineering College
Bachelor of Technology
January 1, 2013 – January 1, 2017
AI Text-to-Web (Frontend) Generator (Langchain)
January 1, 2026 – Present
1. Designed and built an AI Automation Tool that generates responsive HTML/CSS/JS websites from text descriptions, accelerating frontend prototyping. 2. Integrated the Google Gemini API via LangChain to orchestrate code generation logic. 3. Features include a multi-tab code viewer, live website previewing, and an automated ZIP file packager for easy deployment.
SEBI Annual Report: AI Risk Analyst (RAG)
January 1, 2026 – Present
1. Developed a Retrieval-Augmented Generation (RAG) application to automate the analysis of unstructured SEBI regulatory reports and financial statements. 2. Orchestrated a pipeline using LangChain and LLM to extract critical insights regarding compliance failures, penalties, and market risks. 3. Implemented ChromaDB for vector storage and semantic search, enabling the system to answer complex queries based on specific document context. 4. Built an interactive Streamlit dashboard that acts as a virtual "Risk Analyst," reducing manual document review time and improving data retrieval accuracy.
Dynamic Digital Advertising Platform (Vision AI)
January 1, 2026 – Present
1. Tools: Python, OpenCV, YOLOv8, TensorFlow/Keras, ResNet50V2, TFLite. 2. Designed a real-time AI system to dynamically switch digital ads based on audience gender composition in public spaces. 3. Integrated YOLOv8 for person detection and fine-tuned ResNet50V2 (converted to TFLite) for lightweight gender classification. 4. Applied ROI-based filtering via OpenCV to ensure ad targeting only when viewers are actively present. 5. Replaced static time-slot ads with dynamic, context-aware placements, improving engagement and relevance.
Soil Pollution-Associated Diseases Prediction
January 1, 2026 – Present
1. Developed a machine learning model to predict disease risk associated with soil pollution patterns using historical soil quality data and environmental health statistics. 2. Collected soil sample data, analyzed pollutant concentration levels, and correlated them with regional disease incidence using Python and Pandas. 3. Engineered relevant features and trained algorithms like logistic regression and decision trees to classify disease risk groups.
Advanced Data Science with Python
NASSCOM
June 1, 2026 – Present
GEN AI & Agentic AI Internship
Innomatics Research Labs
June 1, 2026 – Present
Python Completion Certification
Innomatics Research Labs
June 1, 2026 – Present
Machine Learning Certification
Innomatics Research Labs
June 1, 2026 – Present
The candidate scored 94% on the 'Data Scientist — Artificial Intelligence' exam, indicating a very strong grasp of the subject matter and related skills.
Strengths
Limitations
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest in applying AI to diverse real-world problems, from advertising and financial analysis to web development and environmental health. This breadth of application, coupled with a focus on automation, aligns well with an 'AI Automation Engineer' role that often requires adaptability and a problem-solving mindset across various domains. The personal nature of the projects indicates self-motivation and a proactive learning approach, which are positive indicators for cultural fit in an innovative environment.
Soft Skills & Operational Fit
The psychometric test score of 236/500 suggests potential areas for development in logical reasoning, work attitude, stress handling, or team collaboration. While the projects demonstrate initiative and problem-solving, these areas would require further assessment during interviews to ensure operational fit within a team environment.