Chirstopher Hanson

Experienced software engineer with over 9 years of professional experience building full-stack systems and 5+ years focused on productionizing LLM/NLP solutions, retrieval-augmented generation (RAG) flows, and scalable model deployment. I design and implement model training and inference pipelines using PyTorch, TensorFlow and Keras, converting prototypes into Dockerized microservices served with Flask and deployed on AWS SageMaker and GKE. I architect data ingestion and pre processing pipelines for text classification, NER, relationship extraction and coreference resolution using spaCy, Hugging Face Transformers, and custom tokenization strategies to improve model precision and recall. I build retrieval layers with Elasticsearch and Redis for low-latency vector search and combine FAISS and ANN approaches to scale embedding-based RAG across millions of documents. I implement end-to-end MLOps: CI/CD for models, automated model validation, Canary and A/B rollout strategies, and observability with Prometheus/Grafana to measure latency and model drift. I leverage SQL (PostgreSQL) and NoSQL (MongoDB/DynamoDB) storage patterns to balance OLTP and document-store needs, and tune queries and indexes to reduce query times under load. I lead cross-functional teams to translate research into product features, mentoring engineers on PyTorch training loops, mixed-precision, and memory-efficient inference. I have strong software development experience in Python and Flask for API layers, Node.js/React for admin and tooling, and integrate secure auth, RBAC and logging for compliance and auditability. I emphasize reproducible experiments with DVC, containerized training images, and artifact registries to accelerate research-to production timelines. I drive cost-aware architecture decisions that balance latency, throughput, and cloud spend — regularly optimizing SageMaker endpoints and batch transforms. I bring pragmatic, test-first engineering: unit and integration tests for model-serving code, load tests for endpoints, and regression checks to prevent performance regressions. I am pursuing roles where I can combine full stack development skills with advanced NLP/LLM engineering to deliver measurable business impact through retrieval-augmented AI solutions.