Open to full-time AI engineering roles

I build AI tools that work in production.

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AI Engineer building production AI systems — from LLM pipelines to deployed APIs, I build things that actually ship.

0+ Live Apps
0+ Projects
0 OSS Library
View My Work GitHub
Open Source Library

llm-behave

pytest plugin for behavioral testing of LLM applications

Stop using LLMs to judge LLMs. llm-behave brings semantic assertions, tone detection, and drift detection to your CI pipeline — all with an offline 80MB model. No API costs. No circular dependencies.

  • Semantic matching — response.mentions("refund") understands meaning, not just strings
  • Tone & intent detection — response.tone("professional") without an LLM judge
  • Drift detection for CI — catch behavioral regressions before they hit production
  • 100% offline — sentence-transformer runs on CPU, ~32ms per assertion
View on GitHub Read Docs ↗
test_support.py 
# Install the library
$ pip install llm-behave[semantic]

# Write semantic tests, not string matchers
def test_support_response(llm):
    response = llm.ask(
        "How do I get a refund?"
    )

    # Understands meaning, not just words
    assert response.mentions("refund policy")
    assert response.tone("professional")
    assert response.intent("helpful")

# Detect drift across model updates
def test_no_regression(drift):
    drift.assert_consistent(baseline="v1.json")
In Production

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Work

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Comprehensive demos of CoT, few-shot, RAG, and agent prompting techniques with benchmarks.

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MCP Server

mcp-llm-behave

MCP server wrapping llm-behave using FastMCP with stdio transport. Run behavioral LLM tests from any MCP-compatible client.

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Databricks Generative AI Fundamentals Certified 2024

AI & LLMs

LC LangChain
OAI OpenAI
GEM Google Gemini
Scikit-Learn Scikit-Learn
HF HuggingFace
RAG RAG / FAISS
MCP MCP / FastMCP
ST sentence-transformers

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Swanand Potnis
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About

Hi, I'm Swanand.

I'm an AI Engineer based in India. I build production AI systems — from LLM integrations and RAG pipelines to testing infrastructure and deployed APIs. Not notebooks that sit on GitHub, but tools with real users.

Today I maintain llm-behave (an open source testing library for LLM apps), run production applications with active users, and ship code daily. Background in data engineering and analytics.

Databricks Generative AI Certified (2024)
2 live applications with active users
Open source library author (PyPI)
Actively seeking full-time AI engineering roles
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Let's build something.

Actively looking for full-time AI engineering roles. Also open to freelance projects and interesting collaborations — AI systems, LLM integrations, and production tooling.

India — Remote worldwide
Actively seeking full-time roles
Also available for freelance projects