Tariq S.

$800K+ earned on Upwork · 107 jobs completed · 100% Job Success · 18000+ hours · Top Rated Plus.

I build production RAG pipelines with measured retrieval accuracy, LLM agents with guardrails and evals, and I audit existing AI builds before they meet real users. Python, FastAPI, Next.js, OpenAI and Claude.

Most of what I am handed already technically works on the founder's machine. The gap is everything between that and paying customers.

For nine years founders have handed me a product and trusted me to own the whole thing, from architecture through the AI layer to the frontend and deployment. Increasingly the work is not building from zero. It is fixing what a fast moving founder or an AI coding tool already shipped, and making it safe to put in front of real users.

Recent builds: a healthcare compliance RAG agent on LangChain, GPT-4o and Pinecone, a real time voice agent handling inbound calls end to end on the OpenAI Realtime API, and a multi agent operations platform that reads from and writes to a CRM unattended.

AUDITS

Often the first thing a client needs is not more code, it is an honest read on what they already have. I run paid technical audits on existing AI SaaS builds and hand back a written assessment: critical issues and technical debt, security and data handling gaps, reliability and error handling problems across webhooks and background jobs, database and schema improvements, cost and latency waste in the AI layer, and a prioritised plan with effort estimates. On one healthcare compliance build the same approach took audit time down 85 percent and human error down 90 percent. If you want the same engineer to implement the plan afterwards, I do that too.

RETRIEVAL THAT HOLDS UP AS THE CORPUS GROWS

Document parsing, semantic and sliding window chunking, embeddings, hybrid search with cross encoder re ranking, tenant and document level filtering so users only ever see what they are authorised to see, and grounded answers with source citations from OpenAI or Claude. Vector databases including pgvector, Qdrant and Pinecone depending on scale and budget. Most RAG systems fail on retrieval, not on the model.

AGENTS THAT RUN UNATTENDED

Tool and function calling, structured outputs, multi agent orchestration with LangGraph and LangChain, retries and clear stopping conditions, human in the loop checkpoints, cost ceilings, and monitoring so you find out an agent is misbehaving before your customers do. I have built conversational voice agents that handle inbound calls end to end, and agentic workflows that read from and write to CRMs without a human in the loop.