arbaz aslam

I am an AI Engineer passionate about building intelligent, production-ready systems that bridge modern Generative AI with robust data infrastructure. I specialize in designing Hybrid RAG architectures, orchestrating autonomous multi-agent workflows, and deploying secure, privacy-first local LLMs. By combining a strong foundation in predictive machine learning with hands-on expertise in automated data pipelines, I focus on delivering end-to-end, data-driven enterprise applications from conception to deployment. I am highly adaptable and driven to build scalable AI solutions that solve real-world business challenges.

Core Expertise

Generative AI & Orchestration: Hybrid RAG Pipelines, Multi-Agent Systems, LangChain, CrewAI, LangGraph, Ollama, Transformers.
Machine Learning & Data Science: Python, Predictive Modeling, XGBoost, BERT, Pandas, NumPy.
MLOps & Deployment: MLflow, Streamlit, GitHub Actions (CI/CD), Google Cloud Platform (GCP), Git.
Pipelines & Automation: n8n, Make.com (API Integrations), Google BigQuery, ETL/ELT Workflows.

Professional Highlights

Autonomous AI Workflows: Engineered multi-agent AI architectures using CrewAI (Researcher, Analyst, Writer) to autonomously execute tasks and collaborate on complex internal workflows.
Privacy-First Implementations: Deployed local-first generative AI chatbots via Ollama, ensuring 100% data privacy and zero data leakage for sensitive enterprise queries.
End-to-End ML Deployment: Designed and deployed predictive ML pipelines (XGBoost) and interactive Streamlit web applications, utilizing BigQuery as a serverless feature store and MLflow for lifecycle management.
AI Data Ingestion: Automated enterprise reporting and optimized data pipelines using Python and n8n to clean, transform, and structure raw data into high-quality context for RAG models.

What I'm Looking For

I am seeking a remote Junior or Entry-Level AI Engineering role where I can contribute to building cutting-edge Generative AI applications, intelligent automation workflows, and robust machine learning pipelines in a collaborative, fast-paced environment.