Available for new projects · Remote worldwide

    Hire an LLM & RAG Developer

    Need an LLM and RAG developer who builds features you can actually trust in production? I design retrieval pipelines, citation grounding, and evaluation harnesses so your AI answers are accurate, traceable, and safe — not a demo that hallucinates the moment it meets real data. Remote, senior, and integrated with your existing stack.

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    What I can help with

    • Retrieval pipelines: chunking, embeddings, hybrid search
    • Citation grounding so answers trace back to sources
    • Evaluation harnesses for accuracy and groundedness
    • Hallucination mitigation and output guardrails
    • Prompt/context engineering and optional fine-tuning

    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

    LLM / RAG EngineeringAI Workflow AutomationML Systems & MLOps

    Frequently asked questions

    What does an LLM / RAG developer build?
    An LLM/RAG developer builds the retrieval layer that feeds a language model relevant, up-to-date context from your own data, plus the citation, evaluation, and guardrail systems that keep answers accurate and traceable in production.
    Will a RAG system stop the AI from making things up?
    RAG substantially reduces hallucinations by grounding answers in retrieved sources and enabling citations. Combined with an evaluation harness that measures groundedness and guardrails on outputs, it keeps accuracy high — and catches regressions before your users do.
    Do I need to fine-tune a model?
    Usually not. Retrieval and prompt engineering solve most use cases more cheaply and flexibly than fine-tuning. I recommend fine-tuning only when there's a clear, measured gap that retrieval can't close.

    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.

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