Direct the goal and execution for agentic AI architectures, applying modeling techniques, Reinforcement Learning (RL), Reward Models (RM), and custom classifiers to significantly suppress hallucinations and achieve high-accuracy task execution. Lead, grow, and mentor a highly specialized team of experienced AI/ML software engineers working on advanced algorithm design, model tuning (LoRA/SFT), and agentic deployment. Bridge applied AI research and production deployment by overseeing engineers driving fine-tuning, reward model alignment, continuous evaluation, and agent serving infrastructure.
Leverage telemetry, offline/online benchmarks, and developer tooling to improve product excellence, production reliability, and engineering execution velocity across agentic workflows. Partner closely with developers, product managers, and internal business owners across Google to identify productivity bottlenecks, coverage gaps, and quality issues.
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