Wonksnadle

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I'm particularly interested in data engineering, AI/LLM applications, backend automation, and technically challenging projects where I can contribute directly to building and shipping solutions.

: Data Engineer & AI/ML Developer

[Programming Languages & Technologies]: Python, SQL, PostgreSQL, MySQL, Apache Kafka, Apache Airflow, dbt, Docker, Pentaho, Salesforce REST APIs, Hugging Face, PyTorch, OpenCV, Git

: 1–2 years of relevant experience across professional data engineering, AI/ML development, and research projects.

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- Jobally: https://jobally.pages.dev/ — An AI-powered platform for evaluating technical writeups and handling inconsistent answers through semantic equivalence, validation, and deterministic normalization.
- Data Engineering Pipelines: Built containerized ELT workflows using Docker, Airflow, dbt, and PostgreSQL; worked on Kafka-based real-time streaming and analytics integrations.
- Salesforce Data Integration: Developed idempotent PostgreSQL-to-Salesforce synchronization workflows with schema mapping, duplicate detection, API fallbacks, and data validation.
- AI & Computer Vision Research: Developed document and circuit extraction pipelines using Python, OpenCV, PyMuPDF, and Tesseract, with a focus on performance and extraction accuracy.

: Open to short-term projects, ongoing collaborations, contract work, and remote opportunities. Available for project-based work and flexible engagements.

: I build reliable data pipelines, practical AI applications, and automation systems that solve real-world problems.

My experience spans batch and real-time data processing, ELT/ETL workflows, database integrations, API-based automation, and AI/ML systems. I've worked with PostgreSQL, Python, Kafka, Airflow, dbt, Docker, and Salesforce integrations, with an emphasis on data quality, idempotency, validation, and maintainability.

I also have hands-on experience with computer vision, document processing, semantic evaluation, and NLP-based applications. I enjoy taking complex workflows and turning them into efficient, scalable systems with clean architecture, measurable results.

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