MLOps / AI Engineer | LLMOps | AI Integrations | Data Infrastructure | Python, Docker, Kubernetes, Airflow, MLflow
I focus on MLOps, AI integrations, and data infrastructure roles where software engineering, automation, and practical AI delivery come together.
I want this section to make my positioning legible at a glance: one clear summary, one stack overview, and direct evidence of what I have already shipped in production or adjacent applied settings.
I work at the intersection of MLOps, AI engineering, automation, analytics, and product delivery.
I bring 10+ years in IT across full-stack development, digital product implementation, workflow automation, and recent data science training. My focus is on building practical systems that connect software engineering with AI workflows, data infrastructure, business intelligence use cases, and real operational value.
In commercial work, I have built and maintained digital products, CMS-based solutions, API integrations, authentication systems, and real-time data workflows. At Full-Iron Design, I developed automation and data-processing workflows that reduced manual labor by 20% and led a scraper ETL initiative that facilitated more than UAH 7 million in net profit in 1 year.
My hands-on stack includes Python, Docker, Kubernetes, Git, CI/CD, Airflow, MLflow, Dagster, Terraform, Scikit-learn, Pandas, NumPy, TensorFlow, PyTorch, LangChain, OpenAI SDK, RAG pipelines, Weaviate, PostgreSQL, MongoDB, TypeScript, Vue, and Nuxt.
These are the experience entries I would want a recruiter or hiring manager to scan first when assessing fit for AI-oriented engineering roles.