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ANUBprad/README.md

Anubhab Pradhan - ML Systems Engineer Status Graduating


Building Production-Grade AI Systems

I architect reliable LLM infrastructure, evaluation frameworks that catch failures, and clinical-validated ML pipelines. I audit broken systems at scale and ship code that doesn't fail silently.

Currently scaling clinical ML at Hasprana Health Care Solutions. Previously debugged agentic systems at Lamatic.ai. Published research on LLM benchmarking (ICAC2N 2026).


Projects

Kairos — RAG Evaluation Infrastructure
MukhdaX — Face Provenance & Blockchain Verification
LocalBench — Offline LLM Benchmarking
RedOps — LLM Red-Teaming & Evaluation


Skills & Tech Stack

Languages

Python Go TypeScript SQL

LLM & AI Frameworks

Claude LangChain LangGraph PyTorch Ollama

Production & Infrastructure

FastAPI PostgreSQL Docker Kubernetes Redis

Observability & Tools

Prometheus OpenTelemetry GitHub ONNX

ML & Computer Vision

scikit-learn XGBoost OpenCV


Current Work

Hasprana (Aug 2026 – Present)
U-Net retrain: 0.888 → 0.970 IoU (+9.2%), 70% error reduction. ONNX + INT8 quantization. 75% model size reduction. Clinical validation on 12 images + 251 tests.

Lamatic (Jan 2026 – May 2026)
Traced agent failures (hallucinations, routing errors, reasoning loops). A/B tested fixes. Deployed to production.


Research & Open Source

ICAC2N 2026 — Benchmarking Instruction-Tuned Small Language Models
Compared Phi-3-mini, Mistral-7B, Gemma-2. Finding: metric choice determines model ranking.

LangChain Contributions

  • PR #31802 (merged) — Fixed missing else branch in _evaluate_in_project()
  • PR #38465 (submitted) — KeyError fix in file_tool callback

Competitions

  • Smart India Hackathon 2024 (National Finalist)
  • Gen AI Exchange 2025 (National Finalist)
  • EY Tecathon 6.0 (Technical Lead)

What Differentiates Me

Code Auditing at Scale — 62K+ LOC analysis. God node detection. Coupling analysis. Architecture assessment.

Reliability Engineering — Kairos measures 12+ IR metrics with confidence intervals. RedOps generates forensic evidence. I measure failures, not just ship code.

Clinical Validation — Models validated on real patient data. Inference pipelines quantized, tested, benchmarked. Production-ready code.

Systematic Debugging — Trace agentic execution patterns. Identify hallucinations and routing errors. Fix via A/B testing.


Seeking

Full-time AI/ML engineering roles (June 2027)

Focus: System design, evaluation rigor, production reliability, observable infrastructure.

Email — pradhananubhab25@gmail.com
LinkedIn — anubhabpradhan
GitHub — ANUBprad


Last updated: September 2026

Pinned Loading

  1. kairos kairos Public

    The open-source platform for transparent RAG development, evaluation, experimentation, and explainable AI.

    TypeScript

  2. localbench localbench Public

    Offline-first research framework for local LLM evaluation and specialization. Builds reproducible, repository-disjoint code-retrieval datasets and benchmarks model quality, latency, memory, and foo…

    Python

  3. redops redops Public

    Production-grade AI evaluation and red-teaming platform for testing LLM quality, safety, reliability, and observability with automated evaluations, adversarial campaigns, reproducible workflows, an…

    Python

  4. mukhda-x mukhda-x Public

    MukhdaX — See. Trace. Verify. An AI-powered image provenance system that traces face images across the public web and anchors verifiable evidence on Ethereum.

    Python