gRPC Embedding & LLM Serving Pipeline

gRPC Embedding & LLM Serving Pipeline
February 2026

Production gRPC infrastructure for text embeddings (rpcembed) and vLLM-based LLM serving, forming the AI inference stack that powers semantic search and RAG for agents at Accretional.

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OpenVINO Go Bindings

OpenVINO Go Bindings
January 2026

Idiomatic Go bindings for Intel's OpenVINO Runtime, built for high-performance AI inference in production systems.

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Founding Engineer - Infrastructure & Platform

Accretional
2025 - Present

Authored openvino-go (open-source Go/CGO inference library for Intel OpenVINO), built gRPC embedding and vLLM serving infrastructure, and engineered hybrid vector search for AI agents.

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CT Denoising and Explainability using OT-CycleGAN

CT Denoising and Explainability using OT-CycleGAN
June 2025

Adapted OT-CycleGAN for denoising ultra-low-dose CT scans with explainability features using Grad-CAM and attention visualizations

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AI Engineering Intern

Accretional
2024

Built core features for Brilliant, an AI coding assistant. Worked on RAG pipelines, LLM fine-tuning, and the extension's prompt and deployment systems.

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Academic Lecture Video Summariser

Academic Lecture Video Summariser
June 2024

LLM and RAG-based system for summarizing long-form academic videos

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Whispers of the Heart - AI Therapy Assistant

Whispers of the Heart - AI Therapy Assistant
May 2024

Python-based conversational AI system for journaling and therapeutic assistance

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Senior Machine Learning Scientist

Goldman Sachs
2023

Led ML initiatives for real-time loan processing. Built A/B testing frameworks and optimized system performance at scale.

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Master of Science

University of California, Santa Cruz
2023 - 2025

Focused on machine learning and AI, particularly medical imaging applications. Research includes CT scan denoising and AI explainability. GPA: 3.9

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GeneWeaver

GeneWeaver
December 2023

A parametric hardware generator for DNA sequence alignment implemented in Scala

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Machine Learning Scientist

Goldman Sachs
2020 - 2022

Optimized ML inference pipelines from 200ms to 80ms through quantization and caching. Built LSTM forecasting models and production systems handling 1M+ requests/month at 99.9% uptime.

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Machine Learning Research Intern

Sprinklr
2019

Built LSTM-based sentiment analysis pipelines for social media data. Applied model pruning and quantization techniques for efficient deployment.

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Bachelor of Technology

National Institute of Technology Karnataka, Surathkal
2016 - 2020

Focused on machine learning and AI, with research in medical image analysis including published work on prostate cancer grading. GPA: 9.4 (Honors)

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