Aleksandr Shuravin

AI/ML • Middle • Izhevsk, Russia • UTC+04:00 • In office, Partially remote, Fully remote • Work permits: Russia • Available in 2 weeks • from $60k per year

ML Engineer

25+ years exp

10+ years ML exp

SQL

Python

C++

PHP

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Overview

Technical skills

Timeline

Roles

Overview

A pragmatic generalist developer at a solid junior-to-middle level who builds end-to-end prototype applications combining vision, speech and LLM integrations. The strongest proven skill is practical computer vision and application wiring, evidenced by the VisionService and the image-processing Engine and steps (megabax/ar-ai-assistant/src/vision/detectors.py and megabax/CVContainer/Libraries/Core.py). There is little or no evidence of custom model research, production-grade serving, experiment tracking or measured optimization in public code.

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Technical skills

Languages

SQL

Python

C++

PHP

AI/ML

PyTorch

OpenCV

YOLO

Sentence-Transformers

ChatGPT

DeepSeek

Llama

Mistral

Ollama

LLM

NumPy

OpenAI SDK

TF-Keras

Databases

PostgreSQL

Qdrant

MinIO

DevOps

Docker

Rest API

Grafana

Prometheus

Other

FastAPI

Transformers

MySQL

PyTorch C++

RAG

MS SQL

Git

Chain-of-Thought

Computer Vision

Speech Recognition

Timeline

Software Engineer (ML/AI Research) • Middle

Freelance / Project work • Contractor

Jan 2025 to Present 1 Year 9 Months Izhevsk Partially remote

Built a RAG-based system for financial markets that combines news and technical analysis, performs vector retrieval, and uses LLMs to generate trading signals. Worked with Qdrant and embedding models and used DeepSeek, ChatGPT, and local Llama/Mistral models via Ollama. Added prompt design for structured outputs and implemented quality and hallucination evaluation. Also designed ML pipeline infrastructure for ETL orchestration and monitoring using Airflow, Prometheus, Grafana, Docker, Python, and PostgreSQL.

Python

PostgreSQL

Qdrant

Sentence-Transformers

ChatGPT

DeepSeek

Llama

Mistral

Ollama

Chain-of-Thought

RAG

Prometheus

Grafana

Docker

Backend R&D Developer (Video Analytics) • Middle

Programming Store • Full-Time

Nov 2016 to Mar 2026 9 Years 4 Months Izhevsk In office

Developed backend R&D for an intelligent video analytics system using deep learning and computer vision. Refactored legacy code to speed up delivery of new detectors and built a microservice for video storage, accounting, and archiving. Implemented parking detection and metal defect detection using YOLO-style approaches, and created an OCR pipeline for extracting text from technical drawings. Integrated duplicate-item search with vector retrieval (Qdrant/embeddings) and connected it to 1C via REST API; used PostgreSQL, MinIO, and Docker.

Python

PyTorch

OpenCV

YOLO

PostgreSQL

Docker

MinIO

Qdrant

Sentence-Transformers

Rest API

Programmer / Full-stack Developer • Middle

Freelance / Project work • Contractor

Jan 2001 to Dec 2015 14 Years 11 Months Izhevsk In office

Delivered automation solutions for an аптечная network, retail operations, and an industrial enterprise. Implemented 1C-based functionality for warehousing, retail, and benefits workflows and added data exchange with many cash registers. Built analytical reports to improve cost structure and optimized the 1C cost calculation algorithm to reduce execution time. Integrated 1C with external services via HTTP using C#, C++, PHP, and SQL.

C++

PHP

SQL

Middle AI/ML Engineer Confidence: Medium Generalist

Model Architecture & Training

2/10

How well models are designed and trained

Application-level use of models and basic training example; no custom architectures or training pipelines.

Evidence

megabax/ar-ai-assistant/src/ai/assistant.py: AIAssistant.answer and _call_ollama_http showing LLM inference integration and fallback megabax/CVContainer/OtherExamples/CNNMnistDemo.py: simple Keras model.fit training loop for MNIST

Data Pipeline & Feature Engineering

3/10

How data is prepared for models

Modular image-processing pipeline and utilities for frame processing and step composition.

Evidence

megabax/CVContainer/Libraries/Core.py: Engine.process orchestration of ImageProcessingStep chain megabax/CVContainer/Libraries/ImageProcessingSteps.py: MedianBlurProcessingStep, ContourProcessingStep implementations megabax/CVContainer/Exec/run2.py: assembly of processing steps and custom filter (my_filter)

Experimentation & Evaluation

2/10

How results are measured and tested

Minimal experimentation and testing artifacts; basic smoke tests and a toy train/evaluate example but no experiment tracking or reproducible pipelines.

Evidence

megabax/ar-ai-assistant/tests/test_smoke.py: SmokeTests contains unit-style checks for core components megabax/CVContainer/OtherExamples/CNNMnistDemo.py: train/evaluate flow that prints test accuracy

MLOps & Deployment

2/10

How models are shipped to production

Some operational scripts and runtime checks for models and services; no production serving, monitoring or CI/CD evidence.

Evidence

megabax/ar-ai-assistant/tools/download_vosk_model.py: scripted download, extraction and installation checks for offline speech model megabax/ar-ai-assistant/src/ai/assistant.py: base_url resolution, probing of Ollama and HTTP fallback handling

Computational Efficiency

2/10

How efficiently computing resources are used

Some attention to runtime parameters and lightweight CPU-friendly choices but no measured optimization or GPU/quantization work.

Evidence

megabax/ar-ai-assistant/src/vision/detectors.py: model.setInputParams(size=(416, 416), scale=1/255.0) for YOLO inference tuning megabax/ar-ai-assistant/src/voice/speech.py: selection and handling of a small Vosk model and explicit sample-rate/chunk handling

Research Depth & Innovation

1/10

Depth of research and new ideas

No evidence of novel algorithms, paper implementations or research-grade experimentation.

Evidence

megabax/CVContainer/OtherExamples/CNNMnistDemo.py: standard MNIST tutorial example megabax/CVContainer/Exec/run2.py: heuristic my_filter for contour approximation (handcrafted logic)

Expertise

Computer Vision & Image Analysis• Middle

Audio & Speech Processing• Middle

Conversational AI & Chatbots• Middle

Technologies

Python• since 2016 • Senior

C++• since 2001 • Junior

MySQL

PostgreSQL• since 2016

Rest API• since 2016

ChatGPT• since 2025

OpenCV• since 2016

Qdrant• since 2021

DeepSeek• since 2025

Sentence-Transformers• since 2019

YOLO• since 2016

MinIO• since 2016

FastAPI

Prometheus• since 2025

Computer Vision

Chain-of-Thought• since 2025

Speech Recognition

Ollama• since 2025

OpenAI SDK

MS SQL

Llama• since 2025

Mistral• since 2025

Transformers

NumPy

Git

PyTorch• since 2016

Docker• since 2016

Grafana• since 2025

LLM

RAG• since 2025

TF-Keras

PyTorch C++

Computer Vision• mentioned only

Vision• mentioned only

Recommendations

Develop desktop or prototype AR assistants that combine camera-based detection with local/offline speech and lightweight LLM inference.
Build proof-of-concept computer vision pipelines and tooling for rapid prototyping (image processing step library, detector integrations).
Implement speech-enabled UI features or offline speech-recognition utilities for CPU-constrained environments.

Repositories

The developer's experience in this domain has been verified based on AI analysis of the following repositories:

ar-ai-assistant AgentsDemo CVContainer