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@tzc013
AI Engineer, ML, Deep Learning, LLM, RAG and Automation Expert
Pakistan
Englisch
Über mich
I’m an AI Engineer & Data Scientist specializing in Machine Learning, Deep Learning, Generative AI, LLMs, RAG, NLP, Computer Vision, and Advanced Data Analytics. I build production-ready AI agents, multi-agent systems, automation, RAG pipelines, semantic search, predictive models, and ML solutions. My expertise includes Python, PyTorch, TensorFlow, LangChain, LlamaIndex, Hugging Face, OpenAI APIs, FastAPI, SQL, vector databases, Supabase, Docker, and n8n. I transform business challenges into intelligent, automated & data-driven solutions.
Kompetenzen
Neuro.Forge
offline •
Durchschnittliche Antwortzeit: 1 Stunde
Meine Dienstleistungen
KI-Integrationen
I will build ai agents, rag chatbots and llm automation
Maschinelles Lernen
I will build machine learning deep learning and data science solutions
Portfolio
Arbeitserfahrung
Data Analyst / AI & ML Specialist
COMSATS
Jan 2024 - Present • 2 yrs 8 mos
1. Designed and deployed production-grade AI/ML solutions using Python, PyTorch, TensorFlow, Scikit-learn, and modern MLOps practices, covering predictive modeling, classification, regression, NLP, recommendation systems, and intelligent automation. 2. Engineered advanced LLM and RAG architectures using LangChain, LlamaIndex, Hugging Face, OpenAI APIs, embeddings, vector databases, semantic search, document processing, and retrieval pipelines for context-aware enterprise AI applications. 3. Developed autonomous AI agents and multi-agent workflows capable of reasoning, tool calling, API integration, task orchestration, structured data processing, and business-process automation using LLMs, FastAPI, n8n, and custom backend services. 4. Built end-to-end data analytics and engineering pipelines integrating SQL, Python, Pandas, NumPy, Supabase, APIs, and visualization tools to transform large, complex datasets into actionable insights, automated reports, KPIs, and data-driven decision systems. 5. Optimized and productionized AI applications through model evaluation, prompt engineering, performance optimization, scalable APIs, Docker-based deployment, database integration, monitoring, and continuous improvement, turning experimental AI prototypes into reliable business solutions.
