I am an AI/ML Engineer with 3.5+ years of experience building production-style ML and agentic AI systems. I focus heavily on building reliable AI applications with strict, deterministic guardrails. Recently, I built an agentic system for GL coding that auto-handles 72-78% of financial transactions using a three-tier confidence architecture (deterministic rules → payee-history learning → LLM). I implemented an eval harness to measure calibration, exposing LLM overconfidence and validating the need for human-gated escalations. I have also developed production-ready RAG applications (RAG-Cite-Eval) with document chunking, ranked vector retrieval, and source-snippet attribution, complete with an evaluation harness to measure grounding and citation accuracy. Looking for engineering roles focused on Agentic AI, MLOps, or Data Engineering.
Remote: Yes
Willing to relocate: Yes
Technologies: Python, PyTorch, FastAPI, RAG, Anthropic API, SQLite, Docker, SQL, Vision Transformers
Resume/CV: https://drive.google.com/file/d/1Ze_JN2d0FnA4QUPFQRggvgWpKOP...? usp=drive_link
Email: [email protected]
I am an AI/ML Engineer with 3.5+ years of experience building production-style ML and agentic AI systems. I focus heavily on building reliable AI applications with strict, deterministic guardrails. Recently, I built an agentic system for GL coding that auto-handles 72-78% of financial transactions using a three-tier confidence architecture (deterministic rules → payee-history learning → LLM). I implemented an eval harness to measure calibration, exposing LLM overconfidence and validating the need for human-gated escalations. I have also developed production-ready RAG applications (RAG-Cite-Eval) with document chunking, ranked vector retrieval, and source-snippet attribution, complete with an evaluation harness to measure grounding and citation accuracy. Looking for engineering roles focused on Agentic AI, MLOps, or Data Engineering.