Chernov

About

Anton Chernov

Founder | INSEAD Global Executive MBA'27

Technology executive who builds and scales AI organisations. Two decades of turning deep technical expertise into business outcomes — tripled revenue to $6M+ ARR, led cross-functional AI teams at Sony, and built a venture from zero to equity exit. INSEAD-trained in strategy and leadership, with a rare combination of hands-on engineering depth and P&L ownership.

I operate at the intersection of technology strategy and execution — deciding what to build, how to scale it, and who should run it. From 0-to-1 product launches to enterprise AI transformations, I bring the full cycle: vision, architecture, team building, and commercial outcomes.

Strategy. Scale. Impact. Whether it's a build-vs-buy decision for an AI platform, a revenue growth playbook driven by data, or assembling a world-class engineering team from scratch — I focus on the decisions that move the needle and the execution that proves it.

AI Systems & Machine Learning

Computer vision, NLP, and deep learning solutions
Retrieval Augmented Generation (RAG) and LLM integration
Real-time AI for robotics and embedded systems
End-to-end model development, training, and deployment

MLOps & Infrastructure

Kubernetes-based workflow orchestration
Continuous model training and deployment pipelines
Cloud infrastructure on AWS, GCP, and Azure
Scalable data processing and storage architectures

High-Performance Engineering

Low-level optimization with Rust, C++, and Python
Embedded systems and edge AI deployment
Real-time sensor data processing
Performance-critical systems design

Data & Analytics

Predictive analytics and forecasting models
Data pipeline architecture and optimization
Business intelligence and KPI frameworks
A/B testing and experimentation platforms

Product & Growth

AI-powered product development and strategy
Mobile app development and optimization
Growth strategy and execution
Technical due diligence for investors

Featured AI Work

Real-world AI and engineering impact across industries — from revenue growth and healthcare to fraud detection and robotics

Consumer Platform Revenue Transformation

A consumer platform with 5M+ registered users and 150K daily actives needed a revenue breakthrough. Monthly recurring revenue tripled from $150K to $530K+ ($6M+ ARR) in two years — through data-driven experimentation, pricing innovation, new revenue streams, and a team culture built around ownership and speed.

Product Strategy Revenue Growth Data Analytics A/B Testing P&L

AI for Professional Writers

Professional publishers needed AI-powered search across large document collections. A Retrieval Augmented Generation (RAG) system — complete data ingestion with cleaning, chunking, and embedding for vector-based similarity search — went from concept to production-ready deployment with full CI/CD within weeks.

Python OpenAI/GPT Elastic Search GCP RAG

The Learning Robot

A robotic arm that sees, tracks faces, and learns in real time — powered by deep neural networks running on embedded devices with Apache MXNet. Edge computing meets computer vision. The demo generated 30+ enterprise leads at Embedded World and attracted new open-source contributors.

Deep Learning MXNet Computer Vision Robotics C++

Healthcare AI for Wearable Devices

Elderly care patients wearing sensor-equipped devices needed real-time fall detection — with models that adapted to each individual wearer over time. A healthcare AI platform streaming wearable sensor data, with self-retraining models running from embedded C++ on-device to cloud services. 0-to-1, from research to production.

Healthcare AI Wearables Embedded C++ Self-Retraining ML Edge AI

Financial Fraud Detection

Real-time transaction analysis to catch financial fraud before it causes damage. A machine learning system processing transaction streams, identifying anomalous patterns, and flagging suspicious activity — production AI protecting business-critical financial operations.

Fraud Detection Machine Learning Real-Time Analytics Financial AI

MLOps: Orchestration Tools on Kubernetes

Which orchestration tool should your team pick for ML pipelines on Kubernetes? A comparative analysis of Airflow, Argo, Prefect, Kubeflow and more — based on hands-on production experience across enterprise engagements. The article became a go-to reference for teams evaluating MLOps tooling.

MLOps Kubernetes Airflow Argo Published Article

Guiding Principles

Excellence in AI and engineering requires more than technical skills. These principles guide how I work and deliver exceptional results.

Be cool

Exceptional people are neither ordinary nor dull. They strive to find a niche for their unique abilities and work to inspire others.

If you've gotta think about being cool, you ain't cool.

Keith Richards

Move fast

Exceptional people focus on progressing towards set business goals in small iterations. This allows to be flexible and change direction where needed while keeping sporty, but enduring pace.

The first principle is that you must not fool yourself — and you are the easiest person to fool.

Richard Feynman

Balance

Exceptional people know that almost every hard decision is an act of balance. That's why we ride the wave!

Life is like riding a bicycle. To keep your balance, you must keep moving.

Albert Einstein

Elevate quality

Exceptional people have very high standards. They shape high quality products, services and processes with exceptional, often unexpected quality.

My tastes are simple: I am easily satisfied with the best.

Sir Winston S. Churchill

Always learn

Exceptional people are never done learning and always seek self improvement.

There is only one good—knowledge, and one evil—ignorance.

Socrates

Build trust

Exceptional people are honest with themselves and others. They value the strength of people's relationships over robustness of systems. They try to be helpful, instead of trying to be right.

People don't care how much you know until they know how much you care.

John C. Maxwell

CHERNOV

AI BUSINESS IMPACT