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Rian Gill

@riangill

AI/ML Engineer building production GenAI, LLM, RAG, and scalable backend systems with Python, AWS, OpenAI, and LangChain.

United States

Message

I’m a software and AI/ML engineer focused on building production-ready AI systems, backend platforms, and automation solutions. My experience includes Python, FastAPI, LLMs, RAG, NLP, OpenAI, LangChain, vector databases, AWS, Docker, and CI/CD. I’ve worked on healthcare, fintech, and enterprise solutions, taking projects from architecture and development through deployment and optimization. I’m particularly interested in Generative AI, agentic systems, AI infrastructure, and solving complex real-world problems with practical technology.

Experience

Work history, roles, and key accomplishments

Current

Senior AI/ML Engineer

Current

Clinix360

Feb 2022 - Present (4 years 8 months)

Architected and deployed a production GenAI system for clinical documentation using OpenAI GPT APIs and LangChain, cutting manual charting effort by 45% while maintaining HIPAA compliance. Built a RAG pipeline over patient records and clinical guidelines using pgvector, improving query accuracy and reducing lookup time.

OpenAI API Lang Chain RAG Pgvector fastAPI AWS ECS Docker CI CD Hugging Face Transformers Airflow Datadog Sentry JWT OAuth RBAC Audit Logging

AI/ML Engineer (NLP)

MintsClub

Mar 2020 - Jan 2022 (1 year 10 months)

Built an NLP-powered product search and recommendation engine for e-commerce using HuggingFace Transformers and scikit-learn, improving search relevance and click-through rate. Developed sentiment analysis and product-review categorization models using spaCy and NLTK to surface customer insights.

Hugging Face Transformers BERT scikit learn SpaCy NLTK Pandas Kafka Flask Docker Redis Agile

Software/ML Engineer

Invozone

Oct 2016 - Feb 2020 (3 years 4 months)

Built classical machine learning models (scikit-learn, XGBoost) for fraud detection and credit risk scoring on transactional financial data. Developed a Django-based backend and reporting dashboards to surface model outputs and risk metrics to internal stakeholders.