Elasticsearch-focused Support Engineer with nearly 5 years at Elastic and 12+ years in technical support and application engineering.

I work at the intersection of Elasticsearch search systems, production diagnostics, indexing and data modelling, relevance evaluation, observability, and AI-assisted knowledge workflows.

Most of the projects below are active work in progress. I use them as practical labs for testing search ideas, shaping diagnostics, comparing relevance strategies, and turning support and engineering experience into reproducible workflows.

• Elasticsearch search, mappings, indexing, ingest pipelines, and production diagnostics
• Product search relevance, BM25, hybrid retrieval, semantic search, vectors, reranking, and RAG
• Relevance evaluation with judgment lists, Precision@k, MRR, nDCG, and latency benchmarks
• Search quality gates, explainable query behavior, and evidence-based troubleshooting
• Python, TypeScript, Node.js, Docker, Elasticsearch

Version 3 of my duplicate-detection work for knowledge base articles. This is the current, more operational evolution of the earlier prototype line: it turns the ideas from version 2 into a local control plane with ingestion, resumable embedding backfills, chunking, checkpointed duplicate materialization, a live review UI, and optional remote analysis publishing.

What it demonstrates:

• duplicate analysis as a resumable operational pipeline, not just a one-off experiment
• local-first workflows with optional shared remote analysis snapshots
• hybrid search, embeddings, chunk evidence, duplicate edges, and duplicate clusters in one system
• reviewable duplicate families with evidence and editorial decisions in a browser UI

E-commerce product search relevance lab with Elasticsearch mappings, deterministic ingestion, search_profile enrichment, BM25 strategy comparison, ESCI-based relevance metrics, latency benchmarks, and local search quality gates.

What it demonstrates:

• product search relevance is measured, not guessed
• ingestion quality affects search quality
• ranking changes are compared with Precision@5, MRR@10, nDCG@10, and p95 latency
• search quality gates can catch relevance or latency regressions

TypeScript/Node.js search governance prototype that turns raw e-commerce queries into deterministic Elasticsearch execution plans with filters, boosts, exclusions, strategy routing, and explainable policy traces.

What it demonstrates:

• governed search behavior through policy data
• explainable query rewriting and boosting
• deterministic conflict handling
• safer handling of exclusions such as allergens, blocked categories, or business constraints

Release-intelligence and retrieval app for Elasticsearch technical content, with provenance-aware indexing, hybrid retrieval, metadata filters, evidence snippets, and version-aware search workflows.

What it demonstrates:

• provenance-first retrieval
• hybrid ranking
• metadata-aware search
• evidence-based technical research workflows

Small Elasticsearch/TypeScript lab that turns AI search documentation drafts into an indexed decision system and evaluates findability with practitioner questions, judgment sets, MRR, nDCG, and Precision@k.

What it demonstrates:

• documentation can be tested as a retrieval surface
• AI search concepts can be organized into decision-oriented workflows
• practitioner questions, judgment sets, and ranking metrics make findability measurable

Version 2 of this duplicate-detection line of work: a Streamlit workflow for knowledge base articles using Jina AI embeddings, Elasticsearch hybrid search, reranking, HDBSCAN clustering, and a Docker-based local setup. It was the prototype stage that proved out the retrieval, reranking, and clustering ideas before they were expanded into kcs-control-plane as version 3.

Interactive Google Colab quiz covering Elasticsearch, Kafka, Kubernetes, gRPC, Node.js, and resilience concepts.

• I prefer evidence over guessing.
• I focus on reproducible diagnostics, measurable improvements, and clear communication.
• I enjoy bridging support, engineering, documentation, search relevance, and product thinking.