I'm a Data & AI Automation Engineer specializing in data engineering, automation, and AI systems. I design and implement data architectures, MLOps infrastructure, automation pipelines, and bring AI to the core of operations — connecting extraction, modeling, orchestration, and interface into end-to-end products that turn manual processes into reliable, autonomous workflows.
MLOps & Automated Forecasting Design and integration of end-to-end MLOps pipelines for autonomous demand forecasting, reaching 70-82% forecast accuracy by cluster and removing dependencies on legacy third-party ERP architectures.
Market Price Intelligence Design and development of an internal analytics platform that forecasts wholesale agricultural product prices 52 weeks ahead using per-cluster ML models, benchmarking the market against internal prices and generating automated AI insights to anticipate purchasing decisions.
Agentic Development & AI-Assisted Engineering I build software using AI agents as part of my own engineering practice — spec-driven development, agent harnesses, and iterative loops (Claude Code / Agent SDK) — and bring AI into operational pipelines, such as automated insight generation, to support decision-making.
Stock-Transfer Planning Automation Built and shipped a web tool that purchasing analysts use to plan inventory transfers between stores — in production, cutting each transfer plan from hours to minutes and improving stock-reallocation decisions across stores.
Centralized Orchestration & Infrastructure Deployment of orchestration platforms (Dagster) on dedicated Linux servers to centralize, monitor, and automate critical corporate workflows, reducing operational overhead and standardizing failure monitoring.
DevSecOps & Governance Implementation of version control standards, code quality frameworks, and AI-assisted agentic workflows (Claude AI) to standardize internal software development and guarantee code alignment.
While most of my corporate engineering repositories are private, my core achievements focus on building data products end-to-end:
- Autonomous Demand Forecasting Infrastructure: Designed an end-to-end MLOps platform reaching 70-82% forecast accuracy depending on the cluster, contributing decisively to reducing stockouts from a historical 12-20% range to around 8% for high-priority SKUs.
- Market Price Intelligence Platform: Built an internal analytics platform forecasting wholesale agricultural product prices 52 weeks ahead with per-cluster ML models (XGBoost/LightGBM) and automated AI-generated insights, benchmarking the market against internal prices.
- Stock-Transfer Planning Automation: Built and shipped a web tool that purchasing analysts use to plan inventory transfers between stores, cutting each transfer plan from hours to minutes and improving stock-reallocation decisions.
- Centralized Linux Orchestration Server: Migrated dispersed, manual tracking tasks into a single Dagster instance, automating job control and recovery while optimization saved approximately 4 operational hours weekly per user.
- Governance & Agentic DevSecOps: Standardized development repositories via GitHub, implementing agentic coding workflows with Claude Code / Agent SDK to guarantee code alignment and consistent internal software production.
Technical summaries and non-confidential project architecture details can be provided upon request.
Focused on building reliable data systems that create measurable business impact.