Jaffar A.

Senior AI Agent & RAG Engineer | Python, FastAPI, MCP

Top Rated Plus AI engineer with $100K+ earned, 5,000+ Upwork hours, and a 100% Job Success Score. I build production AI agents and RAG systems that connect company documents, databases, CRMs, email, Slack, APIs, and internal tools—then retrieve trusted answers with citations or execute workflows with the right controls. I can help when you need to move beyond a chatbot prototype and build a reliable AI system that is grounded in your data, integrated with your operations, and maintainable after launch. What I build: • AI agents for operations, support, sales, knowledge, and internal teams • RAG and enterprise search over SOPs, policies, contracts, tickets, product documentation, and databases • Internal AI assistants with semantic search, source citations, conversation memory, and role-based access • MCP servers and secure tool integrations for APIs, databases, CRMs, helpdesks, Slack, and email • AI workflow automation using Python, FastAPI, n8n, webhooks, and REST APIs • Document ingestion, chunking, embeddings, vector search, metadata filtering, and retrieval pipelines • Evaluation, tracing, observability, human approval, error handling, and production monitoring • Deployment-ready backends, dashboards, databases, and cloud infrastructure Relevant experience includes a 516-hour HubSpot automation engagement, AI-driven legal document analysis using NLP and OCR, full-stack AI platforms, recommendation systems, API integrations, and production cloud deployments. Core technologies: Python, FastAPI, OpenAI, Anthropic Claude, LangChain, MCP, PostgreSQL, pgvector, Qdrant, Weaviate, n8n, REST APIs, webhooks, Docker, Kubernetes, AWS, GCP, React, and Next.js. My approach is practical: 1. Identify the workflow, data sources, permissions, and business outcome. 2. Design the smallest production-ready architecture that solves the problem. 3. Build retrieval and tool integrations with clear failure handling and human approval where required. 4. Evaluate answer quality, tool-call accuracy, latency, reliability, and cost. 5. Document the system so your team can operate and extend it. You will receive a system designed for real use—not only a demonstration: grounded outputs, clean integrations, maintainable code, measurable evaluation, and clear operational ownership. Send me your current workflow, data sources, and tool stack. I will help you identify the shortest path to a reliable production AI system.