// portfolio_v3 · senior_mle bangalore · india

$ whoami

Anubhav TIWARI

Senior Machine Learning Engineer

Five years shipping production ML across computer vision, audio signal processing, NLP & MLOps — from research to deployment.

domains

CV · NLP · Audio · MLOps

stack

PyTorch · TF · AWS · GCP

experience

5+ years

Resume

scroll

// 01 about_me

Engineer who turns messy problems into shipped systems

From POCs at the whiteboard to production on AWS & GCP — I build, deploy, and own the lifecycle.

End-to-End Ownership

Ideation → research → development → deployment. I take projects through every stage and own the lifecycle.

Computer Vision

Crowd density estimation, object detection, license-plate recognition, face recognition. Real-time video pipelines.

NLP & Code Gen

Natural-language-to-code models, semantic understanding, prototype research solutions. Published papers in DL.

defpredict(x):

Audio Signal Processing

Voice activity detection, speaker diarization, real-world noisy environments. Signal-level engineering.

Cloud & MLOps

AWS & GCP deployments, Docker, CI/CD pipelines. From notebook to production with monitoring & rollback.

Code Philosophy

Reusable, readable, testable. Reduces redundancy, increases understandability, treats infrastructure as a first-class concern.

// 02 experience.log

A five-year trajectory shipping ML at scale

2023 — Present current

Quantiphi Analytics

Senior ML Engineer

CV Audio Cloud

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Key Responsibilities

Developed crowd density estimation models with 95%+ accuracy in varied lighting
Built ML pipelines for license plate recognition on real-time video streams
Created voice activity detection systems for noisy production audio
Designed speaker diarization models deployed on cloud infrastructure

2022 — 2023

Quantiphi Analytics

ML Engineer · L2

CV Face Rec AWS / GCP

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Key Responsibilities

Led ML initiatives from POC through production deployment
Established face recognition capabilities for enterprise clients
Built object detection models with high precision in real-world scenes
Deployed ML solutions across AWS and GCP infrastructure

2021 — 2022

Verifyer Technologies

ML Engineer

Analytics Modeling

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Key Responsibilities

Designed ML models tailored to varied business problems
Analyzed large datasets to surface actionable insights
Built high-accuracy prediction models for operational use
Delivered data-driven solutions with cross-functional teams

2019 — 2020 origin

RMgX Technologies

ML Engineer

NLP Research

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Key Responsibilities

Conducted research in natural language processing
Published papers on innovative deep learning models
Implemented prototype solutions for academic research
Mentored junior researchers in ML methodologies

// 03 selected_work

Six live demos, not screenshots

Every card runs a real visualization. Click, hover, watch — these are the models I ship.

P.01

Crowd Density Estimation

Real-time density mapping for public spaces. 95%+ accuracy in varied lighting.

PyTorch CV Real-time

people 0

density 0.00

status idle

click anywhere to add a person ↗

P.02

Speaker Diarization

Who-spoke-when in real-world noisy audio.

SPK_0 SPK_1 SPK_2

P.03

License Plate Recognition

Detect & read plates on live video.

detected → KA · 03 · MJ · 4271 conf 0.98

P.04

Code Generation NLP

Natural language → executable Python.

prompt >

P.05

Voice Activity Detection

Speech / silence segmentation, live.

SPEECH

P.06

MLOps Pipeline

CI/CD for ML on AWS with monitoring.

data

train

eval

build

deploy

// 04 technical_skills

The toolkit behind the work

Programming

languages & frameworks

Python
PyTorch
TensorFlow
JavaScript
C++

Data Science

domain knowledge

Computer Vision
Signal Processing
NLP
Statistics
Probability

Cloud & Infra

deployment tools

AWS
GCP
Docker
Azure

Tools

utility belt

Git
OpenCV
NumPy
MySQL

// 05 connect

Got a hard ML problem? Let's talk.

~/anubhav — zsh

linkedin.com/in/abtExp

x · twitter

@overfitowl