Looking for a
AI Integration Specialist
serving Remote Teams?
Not chatbot wrappers around ChatGPT. Actual AI workflows built around your data — RAG pipelines, agents with real tool access, and automated classification that runs reliably in production.
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
Most AI integrations fail because they wrap a generic GPT call in a UI and call it done. I design systems around your actual data — retrieval pipelines that pull from your knowledge base, agents that take real actions in your existing tools, and evaluation loops that catch hallucinations before they reach customers. Typical builds: RAG over internal documentation, contracts, product catalogs or support history, and agents that query a CRM, draft emails, update records and route tickets, with a human approving anything that isn't routine. I work with OpenAI, Anthropic Claude, Groq, and open-source models (Llama, Mistral) depending on cost, latency, and data privacy requirements. On the cost side, I help you select the right model tier for each task — a small fast model for classification, a larger one only for generation — and add semantic caching to avoid paying for the same query twice. With the right model per step, caching and batching, many workloads run for tens to a few hundred dollars a month in API costs; I estimate yours from real traffic before we build.
Common Questions
What is RAG and why does my business need it?
Retrieval-Augmented Generation lets an LLM answer questions using your private documents instead of just its training data. Instead of a generic chatbot, you get one that knows your product catalog, your contracts, or your internal SOPs. I build the ingestion pipeline, vector store, retrieval layer, and evaluation framework so you know when the system is working and when it needs improvement.
How much does running an LLM in production cost?
It depends heavily on volume and model choice. For most SMB use cases — internal knowledge bases, support routing, document summarization — you are looking at $50–$300/month after optimization. I help you select the right model tier per task, add caching for repeated queries, and set up spend monitoring so there are no surprise invoices.
Can you integrate AI into an existing product?
Yes. I have added AI features to Laravel apps, Next.js platforms, and Telegram bots as additive layers — no rewrite required. The integration point is usually a new API route that calls the AI layer and returns structured results to the existing frontend. Typical timeline: 2–4 weeks from spec to production.
Ready to start your AI Integration Specialist project?
In Remote Teams or working across timezones, you get a senior engineer who ships clean, working code and stays accountable for it.