t
tzc013

Neuro.Forge

@tzc013

AI Engineer, ML, Deep Learning, LLM, RAG and Automation Expert

Pakistán
Inglés
Parte de la información aparece en idioma inglés.
Sobre mí
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.... Lee más

Habilidades

t
tzc013
Neuro.Forge
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Revisa mis servicios

Integraciones de IA
I will build ai agents, rag chatbots and llm automation
DeUSD 95 / proyectoMás información
Aprendizaje automático
I will build machine learning deep learning and data science solutions
DeUSD 125 / proyectoMás información

Porfolio

Experiencia laboral

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.