SHAIBAN PEDHIWALA

I am an AI systems builder focused on designing, deploying, and orchestrating production ready intelligent workflows. My work centers on bridging the gap between raw machine learning models and dependable software systems building end-to-end pipelines that ingest messy operational data, execute multi-step logic via agentic frameworks, and deliver deterministic, measurable business outcomes.

My development stack spans workflow automation (n8n, custom API integrations), local and cloud-based model inference (Ollama, VLLM, OpenAI/Anthropic APIs), and retrieval augmented generation (RAG) architectures backed by vector stores. Having worked on projects ranging from autonomous event-matching engines and lead enrichment agents to local execution frameworks using lightweight open-source models, I prioritize low latency, cost efficiency, and rigorous prompt and context architecture.

I enjoy building full lifecycle AI systems: setting up persistent memory structures, defining function calling tool registries, handling fallbacks for edge-case failures, and optimizing token consumption for cost effective scale. Whether deploying local models on dedicated hardware or architecting cloud native multi-agent swarms, my goal is creating robust AI systems that run reliably in real world operational environments.