You will get a production-ready RAG & LLM backend system
Top Rated
Top Rated
Project details
You will get a production-ready RAG & LLM backend system designed for real-world deployment, not demos. I build scalable AI backends that connect large language models to structured and unstructured data with reliability, security, and performance in mind.
This project is built for teams that need more than a simple chatbot. Whether you're integrating AI into an internal tool, customer-facing platform, analytics system, or enterprise workflow, the system is designed to be modular, secure, and extensible.
Each tier represents a complete solution for a defined project scope. Smaller tiers cover focused AI integrations with controlled data sources. Higher tiers support multi-source ingestion, hybrid retrieval, scaling strategies, monitoring, and production hardening.
The result is a stable, API-driven AI backend that can be deployed, maintained, and extended confidently.
This project is built for teams that need more than a simple chatbot. Whether you're integrating AI into an internal tool, customer-facing platform, analytics system, or enterprise workflow, the system is designed to be modular, secure, and extensible.
Each tier represents a complete solution for a defined project scope. Smaller tiers cover focused AI integrations with controlled data sources. Higher tiers support multi-source ingestion, hybrid retrieval, scaling strategies, monitoring, and production hardening.
The result is a stable, API-driven AI backend that can be deployed, maintained, and extended confidently.
Programming Languages
JavaScript, Python, TypeScriptCoding Expertise
Localization, Performance Optimization, SecurityWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$3,000
|
Advanced
$6,000
|
|---|---|---|---|
| Delivery Time | 10 days | 20 days | 30 days |
Number of Revisions | 1 | 2 | 0 |
Design Customization | - | - | - |
Content Upload | - | - | - |
Responsive Design | - | - | - |
Source Code |
Frequently asked questions
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Hyehyeon M.
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"Deep expertise in LLMs & Program Systems. Highly recommended."
I requested a technical review of my documents(LLMs, Program Synthesis, Reinforcement Learning), and the quality of his feedback exceeded my expectations. He provided detailed and profound answers specifically on the drafts I submitted.
He clarified complex concepts regarding LLM and Program Synthesis, advising on the accuracy of terminology, current trends, and technical feasibility. His insight into graduate-level research topics, including specific papers and research direction, was incredibly impressive. I look forward to working with him again.
I requested a technical review of my documents(LLMs, Program Synthesis, Reinforcement Learning), and the quality of his feedback exceeded my expectations. He provided detailed and profound answers specifically on the drafts I submitted.
He clarified complex concepts regarding LLM and Program Synthesis, advising on the accuracy of terminology, current trends, and technical feasibility. His insight into graduate-level research topics, including specific papers and research direction, was incredibly impressive. I look forward to working with him again.
MF
Muhammad Umair F.
Dec 29, 2025
Build a Formal Verification & Logic Consistency Agent for LLM-Based Systems
Excellent with deep expertise in AI safety and formal verification. Delivered a robust system for validating LLM reasoning using formal methods and SMT solving. Communication was clear, work was high quality, and delivery was on time. Highly recommended for complex AI, agent, or verification-related projects.
About Muhammad Umar
AI Systems Engineer | LLM Inference RAG Agentic AI | Compilers
100%
Job Success
Lahore, Pakistan - 7:49 pm local time
I build production AI systems and tools for great performance, accuracy and reliability: LLM inference and runtimes, RAG pipelines, and agentic systems. Faster and correct code analysis and transformations within compiler/runtimes for verification and optimization purposes.
Recent work: a local LLM runtime that saved 541 seconds through KV cache optimization, a RAG classification system at 95%+ accuracy, and an AI agent querying SQL databases with millions of records.
Whatever you're building, I've likely engineered the layer underneath it.
𝗟𝗟𝗠 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 & 𝗥𝘂𝗻𝘁𝗶𝗺𝗲
• Local deployment: llama.cpp, Ollama, vLLM, GGUF, TensorRT-LLM, MLX
• Models: LLaMA, Mistral, Phi, Qwen, DeepSeek, Hugging Face Transformers
• Optimization: KV cache, speculative decoding, continuous batching, tensor & pipeline parallelism
• Fine-tuning: LoRA, QLoRA, PEFT, RLHF, DPO, distillation, quantization
• GPU: CUDA, cuDNN, TensorRT, multi-GPU, distributed inference
𝗥𝗔𝗚 & 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹
• Vector stores: FAISS, Pinecone, Milvus, ChromaDB, Qdrant
• Search: hybrid search, dense retrieval, BM25, GraphRAG, knowledge graphs
• Embeddings: OpenAI, Gemini, Mistral, open-source models
• Delivered: real-time RAG classification system, 95%+ accuracy in production
𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜
• Frameworks: LangChain, LangGraph, MCP
• Capabilities: tool calling, function calling, memory systems, reflection, self-correction, planning
• Delivered: AI agent over SQL databases with millions of records, 5-star review
𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
The layer most AI engineers never touch, and the reason my systems hold up when others break.
• Languages: Python, Rust, C++, C
Compiler optimizations, static analysis, instrinsics, loop unrolling, verification
• Runtime: CUDA, cuDNN, NCCL, LLVM, MLIR, JIT, AOT
• Low-level: SIMD, AVX, SSE, OpenMP, OpenCL, lock-free programming, POSIX, epoll
• Profiling, benchmarking, kernel optimization, distributed training
𝗪𝗵𝘆 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗺𝗲
① PhD in compiler and static analysis: I know if the system is right, not just if it runs
② 10 years at Microsoft: I know what production reliability actually requires
③ Full-stack depth: from CUDA kernels to agent orchestration to RAG pipelines
④ Every project delivered, every client satisfied
I work with funded startups, scale-ups, and engineering teams building serious AI infrastructure across any industry.
𝗧𝗲𝗹𝗹 𝗺𝗲 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸𝗶𝗻𝗴.
𝘼𝙧𝙚𝙖𝙨 𝙄 𝙬𝙤𝙧𝙠 𝙖𝙘𝙧𝙤𝙨𝙨:
LLM Inference & Serving: llama.cpp, Ollama, vLLM, GGUF, MLX, TensorRT-LLM, Hugging Face Transformers
Model/Code Optimization: quantization, LoRA, QLoRA, PEFT, fine-tuning, distillation, prompt tuning, RLHF, DPO
Inference Engineering: GPU inference, multi-GPU, tensor parallelism, pipeline parallelism, KV cache, speculative decoding, continuous batching
Retrieval & RAG: hybrid search, vector search, GraphRAG, knowledge graphs, embedding models, FAISS, Milvus, Pinecone, ChromaDB, Qdrant
Agentic AI: multi-agent systems, MCP, tool calling, function calling, memory systems, workflow engines, planning, reflection, self-correction
Systems & Runtime: C++, C, Rust, Python, LLVM, Clang, MLIR, static analysis, memory management, JIT, AOT, thread scheduling, lock-free programming
Performance Engineering: SIMD, AVX, SSE, OpenMP, OpenCL, CUDA, profiling, benchmarking
GPU Computing: cuDNN, TensorRT, NCCL, kernel optimization, distributed training, distributed inference, compiler optimizations
Steps for completing your project
After purchasing the project, send requirements so Muhammad Umar can start the project.
Delivery time starts when Muhammad Umar receives requirements from you.
Muhammad Umar works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Scope Definition
Define objectives, data sources, expected scale, performance constraints, and deployment environment. Finalize system boundaries and confirm architecture direction before development begins.
Architecture & Retrieval Design
Design the RAG pipeline structure including ingestion flow, embedding strategy, retrieval logic, prompt orchestration, and API interaction model aligned with project scope.