hi, i’m arovex nova

About me

I design, train, and deploy AI models that turn data into real-world decisions — from computer vision to large-scale machine learning systems.

See my work

Three futuristic humanoid robots with white armor-like bodies and sleek helmets featuring visor-like designs stand side by side against a muted blue background.

My journey

About me

Building the backbone of modern AI—delivering production-grade systems with mathematical rigor and operational excellence

Jan 2022 — Present

PresentSenior AI Engineer [ Neural Dynamics ]

Architecting distributed training systems and leading the deployment of production-grade LLM pipelines.

June 2019 — Dec 2021

ML Infrastructure Engineer [ DataScale Labs ]

Optimized large-scale data ingestion and automated MLOps workflows for high-frequency trading models.

Jan 2017 — May 2019

Computer Vision Researcher [ Visionary Tech ]

Developed state-of-the-art object detection algorithms for autonomous drone navigation and edge computing.

Jan 2015 — Dec 2016

Junior Data Scientist [ Insight Corp ]

Built predictive analytics dashboards and performed feature engineering on multi-terabyte datasets.

June 2012 — Dec 2014

Data Analyst Intern [ Quantum Analytics ]

Assisted in statistical modeling and data cleaning for large-scale consumer behavior studies.

Step by step

My Process

I integrate deep architectural research, rigorous data strategy, and production engineering to build resilient AI systems.

01. System Audit & Discovery

2–3 Weeks Mapping the infrastructure

I begin with an in-depth audit of your data landscape, current infrastructure, and core business objectives. This foundational phase identifies technical constraints and sets the architectural direction for the project.

02. Architectural Strategy

2–3 Weeks Mapping the infrastructure

Together, we develop a comprehensive technical roadmap. I design the neural architecture and data flow, establishing clear performance benchmarks—such as latency thresholds and accuracy targets—required for success.

03. Engineering & Deployment

8–12 WeeksBuilding production-ready models

The development phase moves through focused sprints of training, fine-tuning, and rigorous testing. I transform theoretical designs into scalable, production-grade AI models integrated into your live environment.

04. MLOps & Evolution

OngoingContinuous optimization

Post-deployment, I implement continuous monitoring and MLOps pipelines to prevent model drift. We constantly measure and refine the system, ensuring the AI remains accurate and scalable as your data demands evolve.

(212) 555-7398 hello@arovex.com

245 Fifth Avenue, Suite 1800New York, NY 10016, USA

testimonials

Hear From My Happy Customers

“They delivered not just a design, but a complete brand experience. Strategic, creative, and incredibly detail-oriented.”

Amelia Wright

Head of Marketing
London, United Kingdom

“The collaboration was seamless from start to finish. Their UX decisions significantly improved our product engagement.”

Steven Jobs

CEO of Krim Co
California, USA

“A rare combination of technical expertise and artistic vision. The final result felt premium and purposeful.”

Hannah Lee

Creative Director
Studio Kinetic

“They delivered not just a design, but a complete brand experience. Strategic, creative, and incredibly detail-oriented.”

Amelia Wright

Head of Marketing
London, United Kingdom

“The collaboration was seamless from start to finish. Their UX decisions significantly improved our product engagement.”

Steven Jobs

CEO of Krim Co
California, USA

“A rare combination of technical expertise and artistic vision. The final result felt premium and purposeful.”

Hannah Lee

Creative Director
Studio Kinetic

Years of Practice, Hundreds of Deployments, and Satisfied Partners

25+

Models in Production

15M+

Daily Inferences

40%

Latency Optimization

500TB

Data Orchestrated

99.9%

System Uptime

Our skills

Tech Stack / Tools

I fuse scalable AI architecture, data-driven strategy, and real-world deployment expertise to build reliable intelligent systems.

Languages

Python

C++

JavaScript

98

/100

Frameworks

PyTorch

TensorFlow

Scikit-learn

96

/100

Data

Pandas

NumPy

Spark

99

/100

MLOps

Docker

Kubernetes

MLflow

82

/100

Cloud

AWS

GCP

Azure

86

/100

FAQ

Frequently Asked Questions

Your questions about our process, services, and workflow—answered.

1

How does your design process work?

Our process includes discovery, strategy, design, feedback, and delivery — ensuring clarity, collaboration, and results at every stage.

2

How long does a typical project take?

A typical project takes 4 to 8 weeks from kickoff to final delivery, depending on the scope, complexity, and specific requirements of the work.

3

Do you work with startups or only established brands?

We work with both startups and established brands, tailoring our approach to fit your specific stage of growth, goals, and resources.

4

Can you handle custom or complex requests?

Yes, we regularly handle custom and complex requests.

About me

About me

My Process

01. System Audit & Discovery

02. Architectural Strategy

03. Engineering & Deployment

04. MLOps & Evolution

Hear From My Happy Customers

“They delivered not just a design, but a complete brand experience. Strategic, creative, and incredibly detail-oriented.”

Amelia Wright

“The collaboration was seamless from start to finish. Their UX decisions significantly improved our product engagement.”

Steven Jobs

“A rare combination of technical expertise and artistic vision. The final result felt premium and purposeful.”

Hannah Lee

“They delivered not just a design, but a complete brand experience. Strategic, creative, and incredibly detail-oriented.”

Amelia Wright

“The collaboration was seamless from start to finish. Their UX decisions significantly improved our product engagement.”

Steven Jobs

“A rare combination of technical expertise and artistic vision. The final result felt premium and purposeful.”

Hannah Lee

Years of Practice, Hundreds of Deployments, and Satisfied Partners

Tech Stack / Tools

Languages

Python

C++

JavaScript

Frameworks

PyTorch

TensorFlow

Scikit-learn

Data

Pandas

NumPy

Spark

MLOps

Docker

Kubernetes

MLflow

Cloud

AWS

GCP

Azure

Frequently Asked Questions

Arovex

Let’s Shape Your Next Idea