Sainivas G

About Sainivas G

My work in AI sits at the intersection of engineering, cognition, and mathematical structure. During my Master’s, I became fascinated by how complex behavior emerges from simple rules whether in network science, probability, or non-constructive ideas like the Axiom of Choice. That perspective shaped how I now think about intelligence: not as a single mechanism, but as a landscape of latent pathways that must be activated at the right moment.At Capital One, I built production-grade agentic systems using LangChain/LangGraph, RAG pipelines with FAISS, MCP-based tool orchestration, and Neo4j reasoning layers for high-throughput fraud intelligence. Designing AI under strict latency, safety, and interpretability requirements taught me that real-world AI is not “a model,” but an ecosystem of agents, retrieval systems, graphs, vector stores, and humans working together to produce reliable reasoning.At Handshake AI, I shifted toward evaluation and spent hundreds of hours analyzing LLM failures across reasoning, extraction, and multimodal tasks. I noticed a pattern: models often knew the right answer but failed to activate the reasoning needed to retrieve it. A subtle change in phrasing, token order, or context would flip the model from correct to incorrect. When I later read “Base Models Know How to Reason, Thinking Models Learn When,” it gave language to what I had been observing most hallucinations are not knowledge gaps, but timing failures.The reasoning exists in the weights; the challenge is triggering it consistently. This mirrors the Axiom of Choice: a solution may exist, but the mechanism that selects it is not guaranteed. Today, I focus on building AI systems where reasoning is a controllable process where agents externalize decision pathways, RAG scaffolds latent retrieval, and evaluation measures not only what a model knows, but when it chooses to apply that knowledge.I’m especially interested in agentic architectures, reasoning research, and hybrid systems that bring structure into model cognition. If you’re working on pushing AI toward deeper and more reliable forms of reasoning, I’d love to connect.

Experience

1. Senior Software Engineer

   Capital One

   Dec 2023 — Present

Education

• Jawaharlal Nehru Technological University

  Bachelor of Technology - BTech, Computer Science

  2017 — 2021

• University at Albany

  Master's degree, Computer Science