Hi, I am Akshat Dhaundiyal(yes, that's me! ☻).

bridging
Applied Machine Learning Architecture
India (GMT +5:30)
● LIVE SYSTEMS
MBA CANDIDATE

Software & AI systems should be auditable.

“AI Systems & Product Strategy (Verb & Noun): a disciplined craft where mathematical rigor meets unit economics, operational adoption, and auditable business outcomes.”

I bridge machine learning engineering with commercial product leadership. Former Lead Analytics Architect at EXL Service (recovered +$12M in commercial insurance audit exposure) currently pursuing an MBA to lead high-leverage AI products.

test operating principles
Core Operating Principles (Interactive)click to test!

Currently cooking ☺︎

Active Build • v0.4
Applied ML & Vision // 2024 — Present

Media Chronicle: On-Device GenAI Memory

Native Desktop Photo Memory Intelligence with Local YOLOv8 & Zero External Cloud Dependencies

Inference TargetSub-100msLocal Neural Retraining & Inference

Designing and training a local-first desktop memory intelligence application. Instead of paying recurring monthly subscription fees and uploading personal family photos to third-party corporate servers, Media Chronicle executes neural face recognition and semantic embedding search directly on consumer CPU/GPU hardware.

Current Sprint Focus:

Currently refining int8 ONNX quantization & testing v0.4 release on macOS/Linux.

Flutter (Dart)PythonYOLOv8PyTorchDesktop IPCuv
GitHub Alpha

Recently Made ▶

All Works
Enterprise AI & SystemsSTUB REF: GLM-4921-99210-EXL
GATE 04 • ZONE A•AUDIT-2024•2023 — 2024
Lead Analytics Architect (Data Scientist) — EXL Service

Actuarial Pricing & GLM Triage Engine

Decomposing Enterprise Policy Risk into a Two-Stage Statistical Triage Matrix

Decomposed commercial insurance policy audits into a two-stage statistical triage matrix (Binomial GLM frequency × Gamma GLM severity), prioritizing field dispatches and automating zero-variance accounts.

Technical & Statistical Execution
  • •Decomposed classification problem into two distinct statistical targets: a Binomial GLM (logit link) predicting adjustment probability, and a Gamma GLM (log link) estimating recoverable dollar severity.
  • •Engineered an automated Expected Audit Value (EAV = P(Adjustment) × E[Severity]) prioritization matrix.
  • •Built schema contracts and idempotent feature validation pipelines in Python to guarantee production reproducibility across multi-million row quarterly policy dumps.
PythonStatsmodelsActuarial GLMsXGBoostPostgreSQLSQL
Repository
Verified Business Impact
+$12M
Annual Audit Premium Recovery
EXECUTIVE AUDIT // VERIFIED +$12M
Retrospective Note:

"In enterprise AI, stakeholder adoption is governed by explainability and unit economics, not raw test-set F1. Giving underwriting executives verifiable mathematical coefficients enabled them to trust model recommendations across $100M+ risk portfolios."

GLM-4921-99210-EXL
Perforated Ticket Stub • Enterprise AI Architecture
BAGGAGE TAG // NLP & Cloud ArchitectureVTX-77402-NLP
DEPLOYED: 2023 — 2024
ML Engineer & Systems Architect • EXL Service

Claims NLP Triage on Google Vertex AI

Sub-100ms Inference Pipeline Extracting Subrogation & Litigation Threats from Unstructured Notes

Sub-100ms text ingestion and transformer inference pipeline extracting subrogation and litigation threat signals from unstructured insurance adjuster clinical notes on Google Vertex AI.

Pipeline & Cloud Architecture
  • •Constructed a high-throughput text ingestion pipeline using spaCy for custom named-entity recognition and automated PII scrubbing (SSNs, policy IDs, medical codes).
  • •Fine-tuned transformer models and sentence embeddings to classify litigation risk and subrogation recovery flags.
  • •Packaged weights in an asynchronous FastAPI inference container deployed onto Google Vertex AI custom prediction endpoints with auto-scaling.
Google Vertex AIHugging Face BERTspaCySentence TransformersFastAPIDocker
Repository
System Latency SLA
70%
Reduction in Triage Latency
SLA VERIFIED (<85MS)
Retrospective Note:

"Model accuracy on static validation sets is meaningless if cold starts breach SLA. Int8 quantization and container warmup hooks proved 5x more impactful to user adoption than squeezing another 0.5% F1 score in offline experiments."

TAG // VTX-77402-NLPInspection Tag • Google Vertex AI Endpoint

Other Work ⁕

Exploratory Systems & IoT

WashQueue Smart Appliance

IoT Hardware | 2024

Hostel laundry telemetry appliance using local Tuya smart plugs, a 2-min soak debounce algorithm, and native WebSockets broadcasting real-time cycle status.

FastAPIWebSockets (/ws)TinyTuyaSQLiteNext.js
<1sLive WebSocket State Broadcast
Code

Rooftop Solar & Hazard CV

Computer Vision | 2024

Deep learning semantic segmentation isolating rooftop degradation, structural risk markers, and usable solar area from high-resolution satellite imagery.

PyTorchOpenCVFastAPISatellite Imagery
~85%Risk Segmentation Precision
Code

Decentralized Marketplace Broker

Systems | 2023

Multi-vendor inventory synchronization and automated order fulfillment routing engine handling split-order supplier dispatch and automated webhook retries.

PythonPostgreSQLDockerREST Webhooks
100%Multi-Supplier Stock Synchronization
Code

Next.js Full-Stack Portfolio Platform

Systems | 2026

Clean Next.js 15 App Router portfolio engine with authentic physical artifact aesthetic, dynamic Super Admin blog studio, and 1-click Medium cross-posting.

Next.js 15React 19Prisma ORMTailwind CSSDocker
150MBStandalone Google Cloud Run Container
Code

Writing & Papers ✎

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About & Perspectives ⌘

Data Scientist & MBA

I am a Data Scientist and AI product strategist focused on bridging deep machine learning architectures with commercial unit economics.

At EXL Service, I served as Lead Analytics Architect, engineering actuarial GLM pricing triage systems that recovered +$12M in commercial insurance audit exposure, and deploying sub-100ms NLP threat detection microservices on Google Vertex AI.

Currently, I am pursuing my MBA to deepen my command over unit economics, enterprise go-to-market strategies, and product governance. Before that, I completed my B.Tech in Electronics & Communication at MIT Manipal, with specialization in digital signal processing and stochastic modeling.

“I believe the best machine learning models are the ones that actually make it to production, earn the trust of human underwriters, and deliver measurable ROI.”

LinkedIn•GitHub•Twitter (𝕏)
akshatdhaundiyal@gmail.com