Greater Seattle Area
Machine Learning Engineer building production AI systems, GenAI applications, and scalable ML infrastructure. Experienced in deploying machine learning at scale across cloud-native environments, with a focus on MLOps, LLM applications, and distributed data platforms. My background spans Machine Learning, MLOps, Data Engineering, and Generative AI, with experience designing and deploying systems that operate reliably at scale. I've built high-throughput data platforms processing hundreds of millions of daily events, automated ML training and deployment workflows, developed AI data quality frameworks, and delivered cloud-native infrastructure supporting production machine learning workloads. Recent projects include AgentEval AI, an autonomous AI quality monitor for Microsoft Copilot built at the Microsoft Agents League Hackathon 2026 on Foundry IQ using a 4-step reasoning pipeline with LLM-as-Judge scoring, statistical drift detection, and automated root cause analysis. Also built ArchIQ, a GenAI platform generating compliance-aware pipeline architectures across AWS, GCP, and Azure using a 7-pass rule engine, RAG via pgvector, and Claude API in under 3 seconds. Core strengths: - MLOps and ML training infrastructure at production scale - LLM applications, RAG pipelines, and agentic AI workflows - Event-driven data platforms and distributed systems - Feature engineering, model deployment, and drift monitoring Technologies: Python, SQL, Go, Kafka, Spark, Airflow, dbt, SageMaker, Azure ML, Databricks, Snowflake, Redshift, MLflow, LangChain, Claude API, OpenAI API, pgvector, FastAPI, Docker, Kubernetes, AWS, GCP, Azure, gRPC. Open to ML Engineer, Data Engineer, MLOps Engineer, and AI Engineer roles. Feel free to connect if you are building reliable, scalable ML systems or GenAI applications.