About Me


Aspiring AI Engineer with hands-on experience in Machine Learning, Deep Learning, and Natural Language Processing (NLP). Strong foundation in data preprocessing, feature engineering, model training, and evaluation using libraries like scikit-learn, TensorFlow, and Pandas. Passionate about implementing AI solutions to solve real-world problems and exploring emerging technologies like Generative AI, LLMs, and MLOps for intelligent automation.


Skills

Experience

Education

  • AI/ML : Machine Learning, Deep Learning, NLP, LLMs, Generative AI
  • Frameworks & Libraries : Transformers, Hugging Face, OpenAI API, scikit-learn, TensorFlow
  • LangChain & Vector DBs : LangChain, MCP, ChromaDBI
  • Deployment : Docker, Git, Streamlit, Gradio
  • Core Concepts : Data Preprocessing, Feature Engineering, Model Training, Evaluation, MLOps/b>
  • IT Suppot at Mobilestyx (2024 - Current)

    - Documented business and functional requirements for 10+ projects, improving clarity and reducing rework by 25%.

    - Created 15+ process flows and technical documents to streamline project delivery and onboarding.

    - Supported implementation of 5+ system enhancements, increasing operational efficiency by 20%.

    - Facilitated 30+ stakeholder meetings.

    - Contributed to project scheduling and tracking, reducing delivery delays by 15%.


  • B.E from Goa College of Engineering (2019 - 2023)

    * Strong analytical and problem-solving skills from mechanical engineering.
    * Solid foundation in mathematics, including calculus, linear algebra, and statistics.
    * Proficient in Python, TensorFlow, and Matplotlib for data analysis and visualization.
    * Experience working with real-world, noisy datasets from sensors and equipment.
    * Skilled in applying data science to challenges like predictive maintenance and process optimization.
    * Unique ability to combine engineering expertise with data-driven solutions.
  • Higher Secondary from Jawahar Navodaya Vidyalaya (2017 - 2019)

    * Strong foundation in mathematics, including algebra, calculus, and statistics.
    * Developed problem-solving skills and programming logic through JAVA.
    * Hands-on experience with SQL, allowing me to efficiently query databases and manage data.
    * Knowledge of HTML helps me understand web-based data and create interactive dashboards.
    * My science background has strengthened my analytical thinking and ability to approach problems systematically.

My Services

AI/ML Model Development

Design, train, and optimize machine learning and deep learning models for tasks such as classification, regression, clustering, and natural language processing (NLP).

LLM & Generative AI Integration

Implement and fine-tune Large Language Models (LLMs) like GPT and BERT for text generation, summarization, chatbots, and automation using OpenAI, Hugging Face, and LangChain.

Model Deployment & MLOps

Deploy AI models in production using tools like Docker, FastAPI, and Streamlit. Set up CI/CD pipelines and monitor performance for scalable and reliable AI applications.

Vector Databases & RAG Systems

Integrate vector databases like ChromaDB for semantic search and Retrieval-Augmented Generation (RAG) to power knowledge-driven AI systems.

My Work

Anime Recommendation System (Content-Based Filtering)

Anime recommendation engine that suggests similar anime based on the storyline using cosine similarity. It processes and analyzes a dataset of over 12,000+ anime titles collected through web scraping, and applies natural language processing (NLP) techniques to generate meaningful recommendations.

Stock Price Prediction using LSTM

The Stock Price Predictor leverages machine learning models to forecast stock prices based on historical data. The project involves collecting and preprocessing data, such as historical stock prices, trading volumes, and market indicators, to identify trends and patterns. Using supervised learning algorithms like Linear Regression, LSTM (Long Short-Term Memory), or Random Forest, the model predicts future stock prices with reasonable accuracy. Key features include data visualization to analyze historical trends, feature engineering to enhance predictive performance, and evaluation metrics like RMSE (Root Mean Squared Error) to assess the model's effectiveness.

Low-code/no-code AutoML tool

An end-to-end automated ML app for classification that simplifies the entire machine learning workflow. It handles data preprocessing (missing value imputation, scaling, and splitting), supports multiple classification models, performs evaluation with key metrics, and enables users to download the trained model for deployment. Designed for efficiency, it reduces manual effort and accelerates model development. 🚀

See more

Contact Me

satkarsarvankar400@gmail.com

+91-9307175826

Download CV

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