KA

Open to new opportunities and research collaborations

Kauntey Acharya

AI Automation Engineer | Quantitative Analyst

From theoretical physics research to AI automation and quantitative analysis. I build reliable AI workflows and turn complex data into rigorous, reproducible, and interpretable insight.

Ahmedabad, India · IST (UTC+5:30)

01About

I am an AI Automation Engineer and Analyst at Innodata Inc. I design and build AI automations, evaluate large language models for response quality, role-specific behaviour, and multimodal tasks, and connect tools through APIs, n8n, and Zapier. On my own time I build fully local AI systems, such as RAG pipelines and research-monitoring agents, with Ollama and ChromaDB. I bring the same quantitative toolkit (Python, SQL, PyTorch, statistics, and time-series modelling) to quantitative analysis and finance.

Before industry, I was a pre-doctoral research fellow in theoretical physics at Ahmedabad University. My research focused on compact objects in general relativity, including naked singularities, black-hole mimickers, energy extraction, and high-energy particle collisions, and led to four peer-reviewed papers in journals such as Physical Review D and the European Physical Journal C.

Outside work I read research papers, play chess, and watch far too much sci-fi. I am also curious about neuroscience, cognitive science, and evolutionary biology.

02Experience

  1. AI Automation Engineer & Analyst

    Aug 2024 - Present

    Innodata Inc.

    Designing and building AI automations and workflows, alongside LLM evaluation, response-quality analysis, role-specific behaviour testing, and multimodal AI workflows.

  2. Research Analyst and Writer

    Apr 2024 - Aug 2024

    NetBotPro Technologies

    Research-driven technical scripts, academic content review, SEO-aware writing, tracking recent research, and prompt engineering.

  3. Pre-Doctoral Research Fellow

    May 2022 - Mar 2024

    Ahmedabad University

    Theoretical and numerical research on electromagnetic fields and particle dynamics in curved spacetime, with Prof. Pankaj S. Joshi and collaborators.

03Skills

AI & automation

n8n, Zapier, API integration, RAG pipelines, LLM evaluation, prompt engineering, Ollama, ChromaDB, and Docker

Languages & libraries

Python, SQL, NumPy, Pandas, PyTorch, C++ (basic), MATLAB, and Mathematica

Quantitative methods

Statistical analysis, time-series modelling, Bayesian inference, parameter estimation, and machine learning

Research & writing

Scientific research, research writing, peer-reviewed publishing, literature review, technical writing, LaTeX, and Obsidian

04Projects

  1. ArXiv Research Analysis Agent

    Monitors arXiv every morning (gr-qc and q-fin by default), filters new papers by keyword, and sends a Telegram brief with a yes/no prompt. On “yes” it downloads the PDF, has a local LLM walk through the paper from first principles, and returns a Markdown report.

    • n8n
    • Ollama
    • ChromaDB
    • Telegram Bot API
    GitHub →Live demo →

  2. Local RAG Document Q&A Agent

    A fully local retrieval-augmented generation pipeline. Upload a PDF, TXT, DOCX, or MD file; it is chunked, embedded with nomic-embed-text, and stored in ChromaDB, and questions are answered by llama3.2 under a strict answer-only-from-context prompt. No cloud APIs, and no data leaves the machine.

    • RAG
    • n8n
    • Ollama
    • ChromaDB
    • Docker
    GitHub →Live demo →

  3. Banking & Financial Stock Analysis

    Quantitative analysis of banking stocks: returns, volatility, correlations, and risk metrics.

    • Python
    • yfinance
    • Pandas
    • Quant
    GitHub →

  4. Gravitational-Wave Tests of GR (ppE formalism)

    Bayesian parameter estimation pipelines that test for deviations from General Relativity in gravitational-wave observations, using the parametrized post-Einsteinian (ppE) framework.

    • Parameter estimation
    • Bayesian inference
    • Python
    • Gravitational waves
    GitHub →

  5. Credit Score Data Analysis

    EDA of credit datasets focused on default risk, class imbalance, correlations, and interpretability.

    • EDA
    • Pandas
    • Risk
    GitHub →

  6. Indian Startup Investment EDA

    Exploratory analysis of Indian startup funding trends, sectors, and investor activity.

    • EDA
    • Pandas
    • Visualisation
    GitHub →

05Publications

  1. JMN-1 singularity in weak magnetic field

    M. Azreg-Ainou, K. Acharya, P. S. Joshi

    Eur. Phys. J. C 84, 535

  2. High energy particle collision in vicinity of naked singularity

    K. Acharya, P. Bambhaniya, P. S. Joshi, K. Pandey, V. Patel

    Phys. Dark Univ. Vol. 50, 102101

  3. Energy extraction from Janis-Newman-Winicour naked singularity

    V. Patel, K. Acharya, P. Bambhaniya, P. S. Joshi

    Phys. Rev. D 107, 064036

  4. Rotational energy extraction from Kerr black hole’s mimickers

    V. Patel, K. Acharya, P. Bambhaniya, P. S. Joshi

    Universe 2022, 8(11), 571

06Notes

  • Loading…
All notes →

07Contact

Ping me for opportunities, a research collab, or just a good paper to discuss.