Lead Senior Python Software Engineer | ML/AI Engineer | Tech Lead | Backend Architect
Kyiv, Ukraine
📧 Email: alexandrvirtual@gmail.com | 📞 Phone: +38 097 563-92-54
🔗 GitHub | LinkedIn

Experienced backend engineer and ML/AI specialist with 10+ years in software development, specializing in Python-based backend systems, microservices, machine learning platforms, and data science solutions.
Proven expertise in building scalable ML infrastructure, NLP systems, and computer vision applications with $200K+ annual cost savings.
Strong knowledge of Clojure, MLOps, and active contributor to open source projects.
Proven leadership and architecture design skills, seeking a CTO, ML Engineering Lead, or Tech Lead role to leverage technical and managerial expertise.

• Languages: Python (expert), SQL (advanced), Go (mid), Clojure (advanced), Java, Scala, C (basic), Bash
• Frameworks: Django, FastAPI, Flask, Aiohttp, Asyncio
• Databases: PostgreSQL, MSSQL, MongoDB, CouchDB, Redis, Elasticsearch
• Messaging: RabbitMQ, Kafka, Celery

• ML Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, YOLO v8
• Data Science: Pandas, NumPy, Polars, Data Analysis, Mathematics, Statistics
• NLP & LLM: LangChain, Large Language Models, NLP, Transformers
• Computer Vision: OpenCV, Image Processing, Object Detection, CNN
• Time Series: Forecasting, ARIMA, LSTM, Demand Prediction

• MLOps: Kubeflow, MLflow, Apache Airflow, Model Deployment, CI/CD for ML
• Cloud: AWS (SageMaker, EC2, S3, Lambda, EKS, CloudWatch)
• Containerization: Docker, Kubernetes, Infrastructure as Code
• Monitoring: Model drift detection, A/B Testing, Performance monitoring

• Data Processing: ETL pipelines, Big Data, Statistical modeling
• Visualization: PowerBI, Matplotlib, Seaborn, BI dashboards
• Tools: Git, Docker, Postman, MS Office, Jupyter Notebooks

• Architecture: Microservices, OOP, Functional Programming, Distributed Systems
• DevOps: Docker, Linux, Git, Docker Compose, CI/CD
• Testing: pytest, unittest, TDD, Automated Testing
• Leadership: Tech management, Team development, Cross-functional collaboration

• Built ML platform for smart retail (CV, forecasting, staff optimization)
• YOLOv8 food detection (94% accuracy)
• Time series models → 25% efficiency gains, $200K+ savings
• Task automation & real-time monitoring
• Tech: Python, TensorFlow, YOLOv8, AWS SageMaker, Kubeflow, Airflow, Docker, K8s

• YOLOv8-based CV app for dining automation
• Real-time processing (<100ms latency)
• Analytics dashboard for dietary insights
• Cut manual work by 60%, improved planning by 35%
• Tech: YOLOv8, OpenCV, Python, TensorFlow, AWS, Docker, MLflow

• Built LangChain-based doc processing (96% accuracy)
• Real-time sentiment analysis pipelines
• PowerBI dashboards for exec BI
• 70% manual review time reduction
• Tech: LangChain, Transformers, NLP, Python, PowerBI, SQL, Pandas

• Designed CI/CD ML platform with zero-downtime updates
• Monitoring: MLflow + drift detection
• K8s on AWS, full observability & auto-scaling
• Tech: Kubeflow, MLflow, Airflow, K8s, Docker, AWS, Terraform

• Processed 10TB+ using Pandas, NumPy, Polars
• 20+ models with 92% avg. accuracy
• Custom forecasting algorithms
• Automated reports → 60% faster analysis
• Tech: Python, Pandas, Scikit-learn, Keras, Polars, PowerBI

📍 Sep 2020 – Present

• Backend in Python, Go, Clojure with microservices
• DB & architecture design (MSSQL, Redis, PostgreSQL)
• ML integration: TensorFlow, Keras
• Stack: FastAPI, RabbitMQ, Docker, Asyncio, Git
• Team lead: 8+ engineers, CI/CD, code standards

📍 Mar 2020 – Sep 2020

• Python 2.7/3.8, Scala backend for e-commerce
• PostgreSQL, CouchDB, Django, Kafka, Redis
• Recommender systems with collaborative filtering

📍 Dec 2018 – Mar 2020

• Backend: Python, Aiohttp, RabbitMQ, Redis, Elasticsearch
• App architecture & DB design
• ML for search optimization

📍 Aug 2017 – Dec 2018

• Backend in Django, frontend in AngularJS
• Microservices in Golang
• Analytics for learning platform

📍 Mar 2016 – Aug 2017

• REST API dev with Django, Celery, unit tests
• Code quality & test coverage improvements

📍 Aug 2010 – Dec 2016

• Zabbix monitoring system admin
• Python/Bash scripting for automation
• Server performance optimization

• Migrated to microservices → +30% performance
• High-load DB design → -25% query time
• TDD + CI/CD → -40% production bugs
• Introduced Go → 2x perf. in key services

• ML platform → $200K+ savings
• 94% accuracy in CV for food recognition
• NLP system processed 100K+ docs at 96%
• 20+ models with 92% accuracy
• 60–70% manual task reduction via ML

• 10TB+ big data processing
• Real-time analytics <100ms latency
• BI dashboards → informed decision-making
• +25–40% business efficiency gains

• Zero-downtime deployments with observability
• Scalable ML infra on AWS
• Full MLOps pipelines: deploy, monitor, rollback
• Open source contributor & ML best practices advocate

National University of Water Management and Environmental Engineering, Rivne
Bachelor’s in Hydraulic Engineering and Water Technology
📅 2005 – 2009

• Machine Learning Specialization – Advanced ML, Deep Learning
• AWS Solutions Architecture – ML on Cloud
• Data Science & Analytics – Big Data, Statistics
• MLOps & Model Deployment – Production ML Systems

• Ukrainian – Native
• Russian – Native
• English – A2 (learning, read tech docs confidently)

Machine Learning: TensorFlow, PyTorch, Keras, Scikit-learn, YOLOv8
Data Science: Pandas, NumPy, Polars, Statistics, PowerBI, SQL
NLP & LLM: LangChain, Transformers, LLMs, Sentiment Analysis
MLOps: Kubeflow, MLflow, Airflow, Docker, Kubernetes, Git
Cloud & Infra: AWS (SageMaker, EC2, S3, Lambda, EKS), CloudWatch
Backend: Python, Django, FastAPI, PostgreSQL, Redis, RabbitMQ
Tools: Git, Docker, Postman, MS Office, Jupyter, CI/CD, Linux