Muhammad Ghulam J.

AI agents, RAG systems, and AI chatbots that still work 6 months after launch. That is the whole job, and it is harder than the demo makes it look. I build them on LangGraph, LangChain, n8n, Python, and FastAPI, and every number on this profile was measured against a baseline before the build and a scored test set after it. A US company runs its daily operations on a multi-agent platform I built, now past 1,000 hours in production. I am a 2x Claude Certified Architect (874 on Anthropic's proctored Professional exam, pass mark 720), plus AWS, Azure, and GCP professional certificates and 6+ years of ML foundations as a Top 100 Global Kaggle Master. Every hour on my contracts is delivered by me. Here is how I keep the percentages below honest: I measure the starting point, build a scored test set from your real data, and track the same metric after launch. Most builds below were delivered under NDA, so instead of logos, I offer this: on our first call, I open the evaluation dashboard from a comparable build and show you the measurement behind any figure here. 𝐏𝐑𝐎𝐎𝐅: 1. Enterprise RAG: search time down 70%, retrieval precision up 75%, 8,000+ live queries daily. Skill set: Retrieval-Augmented Generation, LangGraph, LangChain, Pinecone, hybrid search 2. Legal LLM, on-premises: Gemma 3 27B fine-tuned on 400K+ documents, 91% citation accuracy, zero hallucinated citations, 60% lower inference cost. Skill set: LLM fine-tuning, Gemma 3, QLoRA, DPO, vLLM, on-premises deployment 3. Voice AI: 80%+ of tier-1 calls handled end-to-end, under 1-second response, handle time down 45%. Skill set: Voice AI, LiveKit, Deepgram, ElevenLabs, Whisper, Retell, Vapi 4. HIPAA clinical AI: grounded in the hospital's own clinical records, with human review before anything enters the chart. 91% factual accuracy, intake time down 27%, manual documentation down 48%. Skill set: RAG, Claude, PHI handling, AI guardrails, LangSmith, healthcare AI 5. Revenue agents: a multi-agent system that qualifies leads, runs follow-ups, and keeps the CRM current on its own. $340K pipeline in the first 6 months, 1.8x more qualified leads, 48% less manual sales ops, 900+ automated triggers firing daily. Skill set: Multi-agent orchestration, CrewAI, n8n, AI automation, CRM automation 6. Autonomous support: 1 system across 7 channels that hands off to a human the moment confidence drops. 61% of requests resolved end to end, average response 44% faster, engagement up 23%. Skill set: Conversational AI, AI chatbot, OpenAI API, n8n, intent routing 7. The longest receipt: 1 scoped task became a platform now serving 3,250+ users, 1,485 hours, and $58,200, all public in my work history below. Nobody extends a contract to 1,485 hours out of politeness. Skill set: Multi-agent systems, LangGraph, open-source LLMs, FAISS, PostgreSQL, image OCR 𝐓𝐄𝐂𝐇 𝐒𝐓𝐀𝐂𝐊: 1. Models: GPT-6 and GPT-5.6, Claude Fable 5.1 and Opus 5.5, Gemini 3.8, DeepSeek V4.1, Qwen 3.8, Gemma 3, open-weight fine-tunes 2. Agents and orchestration: LangGraph, LangChain, LlamaIndex, CrewAI, OpenAI Agents SDK, Google ADK, Claude Agent SDK, MCP servers, A2A 3. AI automation: n8n, Make, Zapier, webhooks, REST APIs, human-in-the-loop approvals, scheduled agents 4. RAG: Pinecone, Weaviate, Qdrant, FAISS, pgvector, hybrid search, reranking, GraphRAG, Ragas 5. Fine-tuning and serving: LoRA, QLoRA, DPO, vLLM, Hugging Face, on-premises deployment 6. Voice: LiveKit, Deepgram, ElevenLabs, OpenAI Realtime, Retell, Vapi 7. Backend and cloud: Python, FastAPI, PostgreSQL, AWS Bedrock, Azure OpenAI, Azure AI Foundry, GCP Vertex AI, Docker, Kubernetes 8. Observability and evaluation: LangSmith, Langfuse, Arize Phoenix, golden test sets, AI guardrails 𝐇𝐎𝐖 𝐈 𝐖𝐎𝐑𝐊: I ask about the business before I open the editor, because expensive AI mistakes happen in week 1, when a step that should stay human gets automated. Week 1 ends with the success metric, the baseline number, and the test set we score against, agreed before serious money moves. From there, I build in slices you can test, so you see progress every week. You get 1 accountable engineer plus tests, documentation, and a handoff plan from week 1, so continuity never depends on my calendar. 𝐖𝐇𝐎 𝐈 𝐖𝐎𝐑𝐊 𝐁𝐄𝐒𝐓 𝐖𝐈𝐓𝐇: Teams where a workflow is eating hours every week, a scoped $1,500+ first milestone is realistic, and someone in the room can approve a plan. I turn down work I cannot measure, and that discipline is a large part of why my Job Success Score is still 100%. 𝐂𝐎𝐌𝐄 𝐖𝐈𝐓𝐇 𝐀 𝐏𝐑𝐎𝐁𝐋𝐄𝐌, 𝐋𝐄𝐀𝐕𝐄 𝐖𝐈𝐓𝐇 𝐀 𝐏𝐋𝐀𝐍: Hit Invite or send me a message here on Upwork with the workflow you want off your plate or the AI build that is misbehaving. If you have 20 minutes, let's get on a call right now, and you'll leave with an execution plan: what the system owns, what stays human, the first metric we measure, and a timeline. The plan is yours either way. Muhammad Ghulam Jillani AI Engineer, Agentic AI, RAG and Automation