Dima Hurtovenko

Senior AI Engineer

Specializing in large language models, computer vision, and scalable AI infrastructure with RunPod and Hugging Face.

About Me

I'm a Senior AI Engineer with over 8 years of experience in developing and deploying cutting-edge artificial intelligence solutions. My expertise spans across natural language processing, computer vision, and reinforcement learning.

Throughout my career, I've led teams in building scalable AI systems that solve complex business problems. I specialize in optimizing model performance and creating efficient deployment pipelines using technologies like RunPod and Hugging Face.

My passion lies in making AI accessible and practical, turning theoretical concepts into real-world applications that deliver measurable impact.

Technical Skills

Machine Learning

Deep expertise in neural networks, transformers, and reinforcement learning algorithms.

AI Development

Proficient in PyTorch, TensorFlow, and Hugging Face's transformers library.

Data Engineering

Experience with data pipelines, feature stores, and large-scale dataset management.

Cloud & Infrastructure

Skilled in RunPod, AWS, GCP, and containerized deployments with Docker and Kubernetes.

MLOps

Expertise in model monitoring, A/B testing, and continuous deployment of AI systems.

Hardware Optimization

Specialized in GPU acceleration, quantization, and model optimization techniques.

Featured Projects

LLM-Powered Document Analysis

Built an end-to-end system for extracting insights from legal documents using fine-tuned LLMs deployed on RunPod.

RunPod

PyTorch

Transformers

FastAPI

Real-time Computer Vision Pipeline

Developed a scalable computer vision system for manufacturing defect detection with 99.7% accuracy, deployed on edge devices.

Computer Vision

ONNX

TensorRT

Docker

Multimodal AI Assistant

Created a multimodal AI assistant capable of understanding text, images, and audio inputs using Hugging Face's transformers.

Hugging Face

Multimodal

Transformers

React

Distributed Training Framework

Architected a distributed training system for large language models that reduced training time by 78% using RunPod's GPU clusters.

RunPod

Distributed Training

PyTorch

CUDA

Live Demos

RunPod GPU Inference

Real-time demonstration of distributed inference using RunPod's GPU clusters

# Initializing RunPod GPU endpoint

>>> import runpod

>>> endpoint = runpod.Endpoint("YOUR_ENDPOINT_ID")