Sirajul I.

Moving AI from Prototype to Production. Most companies can build a quick AI chatbot in a weekend. Very few know how to build a secure, scalable AI infrastructure that handles enterprise data, avoids hallucinations, and doesn't bankrupt them in cloud costs. That is where I come in. With 17+ years of software engineering leadership, I specialize in the "plumbing" of Artificial Intelligence. I architect, deploy, and secure the cloud environments and data pipelines required to make LLMs and Machine Learning models actually work in production. Core AI & Infrastructure Capabilities: 🧠 LLM & RAG Architecture: Designing Retrieval-Augmented Generation (RAG) pipelines that ground AI in your private data. Stack: Vector Databases (Qdrant, pgvector, Pinecone), LangChain, LlamaIndex, OpenAI/Open-source model integration. ⚙️ MLOps & AI Deployment: Taking models out of Jupyter notebooks and deploying them into highly available cloud environments. Stack: Kubernetes (K8s), Docker, Terraform, CI/CD for ML models, GPU provisioning. 📊 Data Engineering for AI: AI is only as good as its data. I build the robust ETL pipelines that clean, chunk, and embed your data for machine learning. Stack: Python, Rust (for high-performance data processing), PostgreSQL, TimescaleDB, GCP/AWS/Azure Data tools. How We Can Work Together: I am currently taking on clients for Architecture Audits (evaluating your current AI roadmap for scalability and security) and Infrastructure Builds (setting up the DevOps and pipelines for your AI initiatives). Stop wrestling with deployment bottlenecks. Let's build an AI architecture that scales.