Senior Applied ML | GenAI | MLOps

Building production AI systems that deliver measurable outcomes.

I help teams ship reliable AI products faster by combining graph-aware LLM workflows, robust ML engineering, and cloud-native MLOps discipline. My focus is reducing failure risk while increasing measurable delivery impact.

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Impact Highlights

99.9%

Production uptime with 100k+ daily API requests.

230%

Pipeline acceleration through workflow hardening.

35%

F1 score increase in entity extraction systems.

98.7%

OCR extraction accuracy across 5M+ lines.

Featured Projects

lorekeeper

Graph-memory storytelling engine that achieved 61% fewer contradictions in paired evaluation using Neo4j, dual RAG retrieval, and pre-generation guardrails.

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Medical_Doc_Knowledge_Graph_System

Neo4j + FastAPI graph-backed retrieval with a multi-stage Cypher pipeline for grounded RAG.

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Training_Distributed_Systems

Distributed RL platform with Ray and PyTorch PPO using fault-tolerant workers and async rollouts.

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PDF_to_Presentation

AI-enabled automation pipeline to transform instructional PDFs into reusable presentation assets.

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Work_Sample

Practical applied ML and data science implementations showcasing end-to-end delivery skills.

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Beats_MCP

MCP-focused prototype with LEPOR-driven evaluation concepts for practical AI workflow experimentation.

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Core Stack

LLM and Agentic Systems

LangChain, LangGraph, structured outputs, workflow orchestration, and contradiction guard patterns.

Graph and Retrieval

Neo4j 5.x, Cypher, Graph RAG plus Vector RAG, ChromaDB, and causal memory retrieval pipelines.

ML and Evaluation

PyTorch, Transformers, OpenCV OCR, NER pipelines, structured extraction, and benchmark harness design.

Backend, MLOps, and Cloud

Python, FastAPI, Flask, Streamlit, Kubernetes, Docker, OpenTelemetry, AWS, Azure, GCP, Databricks.

Certifications

AWS Certified Machine Learning - Specialty Google Cloud Machine Learning Engineer Azure AI Engineer Associate