I architect and modernize enterprise search & discovery platforms — combining semantic retrieval, vector search, evaluation frameworks, LLM-powered agents, and GraphRAG — with 19+ years delivering production-grade systems at global scale.
Production experience. Practical evaluation. Measurable relevance. Teams trust me to ship systems that stay healthy at scale.
Enterprise-scale search platforms blending relevance, semantic understanding, and intelligent retrieval.
Agent workflows that retrieve, reason, validate, and continuously improve responses.
Moving beyond keyword search with graph-based retrieval and structured knowledge representation.
Deep production experience building, tuning, and evolving large clusters and APIs.
Fine-tuning and adapting modern LLMs for domain-specific use cases with efficiency and control.
Designing secure, observable, and scalable infrastructure for search and AI workloads.
I publish practical AI Search content — hybrid retrieval, evaluation, RAG in production, agents, and GraphRAG.
Deep dives into AI search architecture, agentic systems, GraphRAG, and practical engineering patterns.
How to architect search agents with state management, decision graphs, and validation loops for production-grade agentic AI.
Read on LinkedIn →Moving beyond traditional keyword search with graph-based RAG — structured knowledge, entity reasoning, and semantic context expansion.
Read on LinkedIn →Practical prompt engineering patterns for MongoDB Atlas Vector Search — from query construction to retrieval optimization.
Read on LinkedIn →A curated set of representative enterprise programs. Each focuses on architecture, scale, and measurable outcomes.
Designed and scaled a centralized search platform using Elasticsearch to power discovery across global products.
Built a multilingual AI-driven knowledge system combining relevance models with vector semantic retrieval.
Architected large-scale Elasticsearch clusters for indexing communications supporting regulatory risk detection.
Led migration from Apache Solr to OpenSearch Serverless on AWS with modernization and automation.
In-depth technical notes on AI search, RAG, evaluation, and agentic systems.
How BM25, vectors, and reranking should coexist without breaking latency or relevance.
Read article →Evaluation, chunking realities, retrieval quality, and feedback loops that keep systems honest.
Read article →When to introduce agents, tool use, validation loops, and safety boundaries.
Read article →Validated expertise across search, cloud, AI, and developer tooling ecosystems.
The best AI courses from industry leaders — all completely free. Invest in learning, not tuition.
Claude AI courses — prompt engineering, safety, and building with the API. Direct from the makers of Claude.
Start learning →Google's AI essentials — from foundational concepts to Gemini, Cloud AI, and responsible AI practices.
Start learning →Open-source AI resources — Llama models, PyTorch tutorials, research papers, and developer tools.
Start learning →Deep learning, GPU computing, CUDA, and AI infrastructure — hands-on labs from NVIDIA's DLI.
Start learning →Azure AI, Copilot, OpenAI integration, and enterprise AI — with hands-on sandboxes and certifications.
Start learning →GPT API mastery, prompt engineering, function calling, and building production applications with OpenAI.
Start learning →AI fundamentals, Watson, data science, and enterprise AI — with badges and professional credentials.
Start learning →Amazon Bedrock, SageMaker, generative AI, and cloud ML — with labs and certification prep.
Start learning →Andrew Ng's courses — from ML basics to LLM apps, RAG, fine-tuning, and AI agents. Industry gold standard.
Start learning →NLP, transformers, diffusion models, and open-source ML — hands-on courses from the HF community.
Start learning →