Available for new projects · Remote worldwide

    Vector Database Consultant

    Vector databases are not interchangeable plumbing. The right setup depends on your documents, latency target, filtering needs, update frequency, cost constraints, and evaluation approach. I help teams choose, implement, and tune vector search for production RAG and semantic search systems.

    What I can help with

    • Vector database selection and architecture
    • Embedding model and chunking strategy
    • Metadata filtering, hybrid search, and reranking
    • Retrieval evaluation and relevance tuning
    • Migration from prototype indexes to production infrastructure

    Why work with me

    Remote-first, worldwide

    Fully remote delivery with clients across the US, Europe, and Asia — async-friendly and outcome-focused.

    Timezone overlap

    Based in Islamabad (PKT), with working-hours overlap into both US and European mornings/evenings.

    Fixed-price clarity

    Scoped proposals with clear deliverables and timelines — no open-ended retainers required to start.

    Fast, senior delivery

    Direct work with one experienced engineer — no account managers, no hand-offs, working software each sprint.

    Relevant services

    Frequently asked questions

    Which vector database should we use?
    It depends on scale, filtering, update patterns, hosting preferences, and team familiarity. pgvector is often enough for simpler stacks; dedicated vector databases help when retrieval scale, performance, or operations require them.
    Can vector search work with keyword search?
    Yes. Many production RAG systems use hybrid search, combining semantic similarity with keyword or BM25 retrieval, then rerank results before passing context to the model.

    Let's talk about your project

    Tell me what you're working on and I'll come back with ideas, a scope, and next steps — usually within 24 hours. Free discovery call, no commitment.