Junior AI / GenAI Engineer | LLM Evaluation, RAG & Python Backends

I build reliable LLM applications, agent evaluation systems, RAG pipelines, and Python backends. I focus on deterministic testing, safety guardrails, and production reliability—so agent behavior can be measured, gated in CI, and improved without silent regressions.

CI for AI agents that turns production failures into minimized regression tests.

Local-first evaluation harness for multi-step LLM agents:

• YAML golden suites with correctness, hallucination, tool-call accuracy, latency, and cost
• Production Failure Memory (v0.3.0): redaction before persistence, deterministic clustering, sync/async replay, automatic failure minimization, human-approved golden export, recurrence and resurfacing detection
• GitHub Actions regression gates and local SQLite storage
• Zero-network demo path (no API key required for the flagship Failure Memory demo)

Release verification (v0.3.0): 1115 passed, 1 skipped

Multi-agent Streamlit app for natural-language analysis of uploaded CSVs: quality gates, text-to-SQL (Groq Llama 3.3 70B), EDA/AutoML, forecasting, and ChromaDB RAG follow-ups.

Live demo

Government scheme eligibility navigator for Indian citizens. Rules-engine-first (deterministic), with optional local RAG (ChromaDB) and LangGraph + Groq for conversational guidance. FastAPI + Streamlit.

Freelancer contract risk reviewer: deterministic rules plus Groq for plain-language explanations. FastAPI backend and Streamlit UI for PDF/DOCX/TXT uploads and risk reports.

Python · FastAPI · Streamlit · LangGraph · Groq · Llama 3.3 70B · ChromaDB · SQL · SQLite · GitHub Actions · pytest · Docker

AgentEval v0.3.0 — Failure Memory, deterministic replay/minimization, and CI regression protection for production agent failures.

Install: pip install nishanttyagi-agenteval==0.3.0

• GitHub: nishanttyagi28

Built with a focus on verifiable, working systems. Open to Junior AI Engineer, GenAI Engineer, LLM Developer, and Python Backend opportunities.