Looking for a
RAG Pipeline & Vector Search Expert
serving Remote Teams?
Building production-grade Retrieval-Augmented Generation systems that connect LLMs to your private data. From document ingestion and chunking strategy to vector search, retrieval tuning, and evaluation frameworks.
I've worked remotely since 2021, independently and as part of a NY-based product team, with clients in Armenia, Russia, Europe and the US. The way I work is the same wherever you are: a written spec approved before work starts, written progress updates and short Loom walkthroughs as I go, all code in your Git repository with a clear history, and one person — me — from start to finish. No calls are required; if you prefer a short intro call before we start, that's fine too. Yerevan is GMT+4, so European teams get updates at the start of their day and US East Coast teams have a few hours of overlap.
Service Profile
Availability
Async by default, with working-day overlap for Europe and the US East Coast.
Timezone
GMT+4, async-first Support
Response time
Within 24 business hours
Technical Expertise
Why Hire Me
RAG has a dozen moving parts, and each one can quietly wreck quality. Chunk size and overlap affect recall. Embedding model choice affects relevance. Retrieval strategy — dense, sparse, or hybrid — determines whether the LLM gets the right context. Reranking determines whether the top result is actually the best one. The sources vary — internal documentation, contracts, product catalogs, support archives — and so do the right chunking and retrieval choices for each. I start every RAG project by defining an evaluation set — a set of questions with known correct answers — so we can measure retrieval precision before going to production. I treat hallucination reduction as an engineering problem: structured prompts, source citation, confidence thresholds, and fallback responses when retrieval confidence is low. The result is a system your team can trust, not one that works in a demo and breaks in production.
Common Questions
What vector database should I use for my RAG system?
It depends on your scale and infrastructure. For most teams starting out, pgvector (Postgres extension) is the lowest-friction choice — you already have a database, and it handles millions of vectors without a dedicated service. For larger corpora or more complex similarity search needs, Pinecone or Weaviate offer better performance and filtering capabilities. I help you make this decision based on your data volume and latency requirements.
How do you evaluate whether a RAG system is working?
I build an evaluation set before writing production code — a collection of representative questions with known correct answers. I measure retrieval precision (did we return the right documents?), answer faithfulness (is the LLM's answer grounded in the retrieved context?), and answer relevance. This gives you a baseline to compare changes against instead of relying on subjective impressions.
Can you improve an existing RAG system that gives poor results?
Yes. The most common issues are: chunks that are too large (losing precision) or too small (losing context), a retrieval strategy that does not match query patterns, missing reranking, and prompts that do not instruct the model to stay grounded in context. I audit the pipeline, measure where quality breaks down, and fix the specific components causing the problem.
Ready to start your RAG Pipeline & Vector Search Expert project?
In Remote Teams or working across timezones, you get a senior engineer who ships clean, working code and stays accountable for it.