Production AI Systems • Decision Intelligence • Multi-Agent Orchestration • AI Reliability
Building production-grade AI systems that combine LLM reasoning with deterministic software engineering. Focused on evaluation, orchestration, reliability, and decision support for real-world AI applications.
Former Google GenAI Manager leading multimodal evaluation, AI quality systems, and production AI launches at global scale.
- Multi-Agent Orchestration
- AI Decision Intelligence
- LLM Evaluation & Reliability
- Production AI Infrastructure
- Human-in-the-Loop AI
- Deterministic AI Workflows
- AI Systems for Decision Support
- Agentic AI Systems
- AI Evaluation & Benchmarking
- Frontier Model Reliability
- Decision Intelligence Platforms
- Algorithmic Trading & Investment Systems
- Multi-Agent Architectures
- AI Developer Tooling
A multi-stage AI system that evaluates job opportunities, tailors truthful CVs, performs hiring-manager reviews, benchmarks AI providers, validates outputs, and generates deterministic application artifacts.
Highlights
- Provider abstraction (Codex CLI, Claude CLI, OpenAI API)
- Deterministic orchestration
- Evidence-linked contracts
- Benchmark framework
- Human-in-the-loop decision support
A multi-agent investment decision platform that simulates an institutional investment committee through specialized analyst roles, deterministic orchestration, evidence-based reasoning, and explainable portfolio recommendations.
Highlights
- 17-role investment committee
- Two-round deliberation framework
- Evidence validation
- Risk-aware decision synthesis
- Explainable investment recommendations
Evaluation framework for hallucination detection, factuality, uncertainty scoring, multimodal consistency validation, and long-horizon task reliability.
Operational framework for orchestrating AI workflows, release gating, evaluation pipelines, escalation management, and production AI quality systems.
Evidence-driven research assistant focused on retrieval, contradiction detection, source ranking, and citation-aware synthesis.
Reliable AI systems are built by combining strong model capabilities with deterministic orchestration, rigorous evaluation, and human oversight.
The objective is not simply to generate outputs, but to build AI systems that are observable, reproducible, and trustworthy in production.
Python • FastAPI • LLMs • Agentic Systems • Evaluation Frameworks • Structured Outputs • Pytest • Docker • CI/CD • OpenAI • Anthropic
Building AI systems that bridge frontier models and production software engineering through evaluation, orchestration, benchmarking, and deterministic workflows.
Expanding these principles into decision intelligence across domains including AI evaluation, hiring systems, research automation, and algorithmic investment workflows.
- LinkedIn: link
- Technical writing: coming soon