AI & Software Engineer at AVOCarbon Group (2025-05 – Present)
Designed and deployed production-grade AI systems and automation agents using Python and LLM APIs to orchestrate operational workflows across supply chain, sales, and engineering teams.
- Designed and deployed production-grade AI systems and automation agents using Python and LLM APIs to orchestrate operational workflows across supply chain, sales, and engineering teams.
- Architected and implemented an AI-powered EDI automation pipeline including email ingestion, regex-based document parsing, ETL transformation of unstructured order documents, and automated insertion into PostgreSQL databases via REST APIs, eliminating manual EDI processing.
- Developed a Retrieval-Augmented Generation (RAG) architecture integrating embeddings, vector databases, and semantic search (LangChain / HuggingFace) to enable contextual retrieval across enterprise knowledge bases and improve information accessibility.
- Built conversational AI agents integrating LLM APIs with enterprise databases and backend services to enable natural-language querying of operational data.
- Engineered a technical drawing analysis and costing engine combining OCR-based document digitization (PaddleOCR), LLM-driven extraction of engineering specifications from drawings, and rule-based pricing models to generate automated cost estimations.
- Implemented advanced prompt engineering, memory management, document intelligence, and semantic retrieval techniques to improve response accuracy, contextual relevance while reducing hallucinations in LLM systems.
- Performed model evaluation using precision/recall metrics and hyperparameter tuning to improve prediction accuracy.
- Worked in Agile teams collaborating with data engineers and product stakeholders.
- Business Impact: Automated critical workflows saving 200+ hours/month, reducing operational errors by 90%, and accelerating internal processing times by 70%.
AI & Data Engineer at STECC (2024-07 – 2025-04)
Built document intelligence pipelines combining OCR and NLP techniques to convert engineering documents and technical files into structured datasets for downstream analysis.
- Built document intelligence pipelines combining OCR and NLP techniques to convert engineering documents and technical files into structured datasets for downstream analysis.
- Applied data extraction, transformation, and validation techniques using Pandas and NumPy to prepare datasets for machine learning and operational reporting.
- Contributed to AI-assisted engineering tools capable of analyzing technical documentation and extracting structured specifications to support feasibility and production analysis.
- Collaborated in an agile development environment, iterating rapidly on prototypes and integrating feedback from engineering and operations teams.
End-of-Study Internship at Draxelmaier (2024-02 – 2024-06)
Designed and developed a WPF desktop application to automate production data collection and visualization.
- Designed and developed a WPF desktop application to automate production data collection and visualization.
- Integrated real-time analytics dashboards and reporting modules to support faster operational decision-making.
- Implemented data validation and integrity checks, reducing inconsistencies.
- Designed intuitive UI components focused on usability and efficiency.
- Produced technical documentation for maintainability and scalability.
Advanced Training Internship at Artibedded (2023-06 – 2023-08)
Collected, labeled, and augmented computer vision datasets to improve training robustness and model generalization.
- Collected, labeled, and augmented computer vision datasets to improve training robustness and model generalization.
- Trained and optimized deep learning computer vision models using PyTorch and TensorFlow for plant identification and health analysis.
- Integrated APIs for species classification and automated health assessment.
- Implemented dimension measurement for plant growth tracking.
