Grounded
Retrieval, attribution and evidence before fluency.
Available for high-impact AI systems
Senior AI/ML Engineer specializing in production RAG, agentic workflows, evaluation, safety and cloud-native MLOps.
01 / PHILOSOPHY
“A model is only impressive until it meets production. I build for the moment after the demo.”
Retrieval, attribution and evidence before fluency.
Evaluation gates, confidence thresholds and observable quality.
Versioned, monitored, secure and reversible by design.
02 / SELECTED WORK
Selected from 56 public repositories and professional systems, prioritizing production depth, technical range and direct relevance to AI/ML engineering.
03 / BATTLE RECORD
From enterprise knowledge systems to fraud scoring platforms, each role sharpened a different edge of the production AI stack.
Enterprise RAG, LLM evaluation, Responsible AI, retrieval quality and governance at organizational scale.
Production fraud and risk platform with event ingestion, feature pipelines, hybrid scoring and operational controls.
RAG systems, reproducible evaluation harnesses and auditable agentic tool-execution workflows.
Computer vision and NLP services deployed through Kubernetes, MLflow and cloud-integrated orchestration.
04 / TECHNICAL ARSENAL
A deliberately broad stack, organized around delivering reliable AI products rather than collecting logos.
RAG · LangChain · Embeddings · Agents · Structured Outputs · Prompt Engineering
Groundedness · Hallucination · Relevancy · Bias/Fairness · HITL · Drift
Kubernetes · Docker · MLflow · Airflow · CI/CD · Observability
BigQuery · Redshift · PostgreSQL · AWS · GCP · Vector Stores