Mohammad C.

AI/ML Engineer | AI Agents, RAG Systems, Data Engineering & MLOps

I design and build production-grade AI systems that automate real business workflows, process large-scale data reliably, and deploy machine learning solutions that actually operate in production. My work combines AI Engineering, Machine Learning, Data Engineering, NLP, and Automation Architecture to create scalable systems that are accurate, observable, maintainable, and cost-efficient. Clients typically hire me when they need more than a prototype or chatbot: • AI agents that execute tasks end-to-end • Reliable RAG systems over private/company data • Scalable ML & data pipelines • LLM applications with strong retrieval accuracy • Automation systems integrated into real operations • Production-ready AI infrastructure with monitoring & optimization If you're building AI-powered products, internal copilots, intelligent automation systems, or data-intensive ML platforms, I can architect and implement the full stack. 🧠 Core Expertise AI Engineering & LLM Systems • AI Agents & Autonomous Workflows • Multi-Agent Architectures • RAG (Retrieval-Augmented Generation) Systems • AI Copilots & Internal Assistants • Prompt Engineering & Prompt Chaining • LLM Orchestration & Guardrails • Structured Outputs & Function Calling • Hallucination Reduction Techniques • Context Management & Memory Systems • Multi-Model Routing & Fallback Logic • AI Workflow Automation • NLP Applications & Text Processing • Conversational AI Systems • Document Intelligence & OCR Pipelines • Semantic Search & Knowledge Retrieval • Evaluation, Monitoring & Observability Machine Learning & Data Science • Machine Learning Pipelines • Predictive Modeling • Feature Engineering • Model Deployment & Inference APIs • Real-Time & Batch ML Systems • Recommendation Systems • NLP & Transformer-Based Systems • Deep Learning Infrastructure • AI/ML System Optimization • ML Monitoring & Logging • Statistical Analysis & Data Processing • AI-Powered Analytics Solutions Data Engineering & Infrastructure • ETL / ELT Pipelines • Real-Time Streaming Pipelines • Event-Driven Architectures • Data Lake & Cloud Storage Architectures • API-Based Data Ingestion • Data Cleaning & Transformation • Workflow Orchestration • Distributed Data Processing • Schema Design & Data Modeling • Data Infrastructure for AI Systems • Batch & Streaming Analytics Pipelines • Cloud-Native Data Platforms 🛠 What I Build I design and implement: • Autonomous & semi-autonomous AI agents • High-accuracy RAG systems with vector search & re-ranking • LLM workflows with guardrails, memory & structured outputs • ML infrastructure from training to deployment • ETL/ELT pipelines and event-driven data workflows • Real-time analytics & streaming architectures • AI-powered document processing & research systems • Backend APIs, orchestration services & cloud infrastructure ⚙ Tech Stack AI / ML / NLP: GPT-4.1, GPT-4o, o3, Claude 3.x, Llama 3, Mistral, Hugging Face, Transformers, NLP Pipelines, Embeddings, Semantic Search AI Frameworks: LangChain, LlamaIndex, Agent Frameworks, RAG Architectures, Prompt Orchestration Systems Data & Vector Infrastructure: Pinecone, Weaviate, Chroma, Milvus, Supabase, SQL, Vector Search, Hybrid Retrieval Systems Backend & APIs: Python, FastAPI, Node.js, REST APIs, Webhooks, Microservices Data Engineering: Airflow, ETL Pipelines, ELT Workflows, Streaming Pipelines, Data Modeling, Workflow Automation Cloud & Infrastructure: AWS (Lambda, ECS, S3, RDS), Docker, Serverless, Modal, CI/CD Pipelines, GPU Optimization Automation Platforms: n8n, Make, Custom Orchestrators, API Integrations 📊 What Clients Value • Production-focused AI architecture; not demo systems • AI + Data Engineering expertise combined in one profile • Reduced hallucination & higher retrieval accuracy • Cost-efficient LLM pipelines & inference optimization • Scalable ML & data infrastructure • Clean architecture with maintainable codebases • Reliable automation systems integrated into business operations • Clear documentation, observability & smooth handoff 🚀 Typical Use Cases • AI Agents for Operations & Support • AI-Powered Internal Tools • Enterprise Knowledge Assistants • NLP & Document Processing Systems • RAG Applications over Private Data • AI Automation for Sales & CRM Workflows • ML Infrastructure & Inference Systems • Real-Time Analytics & Data Platforms • AI-Driven Reporting & Research Systems • Scalable Backend Systems for AI Products If you're looking for an AI Engineer, ML Engineer, NLP Engineer, Data Engineer, or AI Automation Architect who can build reliable end-to-end systems from infrastructure to intelligent workflows, I can help.