AI Engineer

Designed and deployed an end-to-end AI platform for automated multi-section professional report generation. Integrated environmental, heritage, and risk data with LLM reasoning and structured-output pipelines.

Built a RAG-powered spatial planning tool using domain-specific regulatory knowledge to produce site-specific layout, parking, and landscaping strategies. Developed PDF and GeoJSON ingestion pipelines for an Azure AI Search semantic-search knowledge base using vector embeddings.

Improved reliability and cost efficiency through hash-based response caching, token-efficient context windowing, and OCR controls. Built an AI evaluation framework with multidimensional quality scoring and edit-diff tracking.

Created CLI and task-runner tooling, Docker environments, and GitHub Actions CI/CD workflows covering testing, image builds, registry pushes, and staged production deployments.

Deployed AI solutions focused on speech recognition and semantic search, integrating backend systems with machine learning inference services.

Implemented and maintained scalable ML training and inference pipelines, optimizing GPU-backed model training for performance and efficiency.

Fine-tuned transformer-based models and adapted architectures for domain-specific NLP use cases using vector-based retrieval systems.

Performed exploratory data analysis on real-world datasets to identify customer behavior patterns and data-quality issues that informed downstream modeling strategies.

Developed regression and classification workflows using feature engineering and GridSearchCV hyperparameter tuning.

Evaluated models with precision, recall, F1-score, and AUC-ROC, and communicated insights to non-technical stakeholders.

Bachelor of Engineering in AI & Data Science.