Professional Overview
MLOps Engineer with 12+ years in software engineering, CI/CD and production AI platforms. I build and operate ML infrastructure end to end: model training and deployment pipelines, feature stores, observability and rollback. Core stack: Python, Docker, Kubernetes, Apache Airflow, MLflow, Terraform, AWS and CI/CD. Recently cut regression time by 60% by rebuilding automated test and release infrastructure for enterprise products. I work best with teams that need a senior engineer to own ML platform reliability and delivery speed. Open to full-time, contract and freelance remote roles at $85/hour.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Accelerated releases and hardened test infrastructure.
- Kept 99% test-infra uptime with Docker, K8s, GitLab CI, Helm.
- Added Ruff, Mypy, Pytest, Prometheus, Grafana controls to CI.
Key Achievements / KPIs
- Cut regression time 60% with Playwright/Pytest and GitLab CI.
- Kept 99% test-environment uptime across releases.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built a Python/Pytest/Selenium automation framework from scratch.
- Built Jenkins CI/CD with API, performance and security checks.
- Cut release validation to hours with parallel test runs.
Key Achievements / KPIs
- Increased regression coverage by 50%.
- Improved automation reliability by 25%.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Established automated testing pipelines for banking microservices.
- Used Selenium, Python, Selenoid, Jenkins and BrowserStack daily.
- Led manual and automated testing of REST APIs for payment services.
Key Achievements / KPIs
- Cut regression from 2 days to 8 hours.
- Reduced production incidents by 40% via REST API tests.
Education
Field of Study: MLOps Engineering
Field of Study: Economy
Areas of Expertise
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
