Erik Peterson

I’m a Senior AI & Data Engineer with 10+ years of experience building enterprise-scale data and AI platforms across Amazon and Humana healthcare ecosystems. I specialize in high-throughput distributed data pipelines, lakehouse architectures, streaming platforms, and AI-ready healthcare data systems using Python, SQL, PySpark, Kafka, Databricks, Snowflake, AWS, Kubernetes, and modern MLOps/LLMOps frameworks.

At Humana, I architected the AgentAssist platform supporting 16M+ medical members and engineered governed RAG-enabled healthcare APIs using VertexAI, Gemini, LangChain, FastAPI, vector embeddings, and semantic search—supporting AI-driven member-service workflows handling 80M+ annual customer interactions. I designed enterprise-scale Snowflake lakehouse architectures for healthcare analytics, AI feature engineering, and operational reporting, improving query performance by 55% across claims, eligibility, pharmacy, and provider domains.

I also build reliability into every pipeline: I implemented observability and automated quality monitoring with Great Expectations, MLflow, Monte Carlo, CloudWatch, and Python validation services—improving SLA adherence and reducing production incidents by 40%. From modernizing legacy ETL into modular cloud-native ELT pipelines (3× deployment velocity, 28% lower infrastructure costs) to establishing AI-ready healthcare knowledge layers (LangChain, OpenAI/Claude APIs, Pinecone, vector embeddings, Snowflake Cortex) and transforming billions of records into analytics-ready datasets (50% runtime reduction), my focus is governed, low-latency outcomes that deliver measurable business and care impact.