Design, build, deploy, and operate production-grade AI agents, automations, and internal tools. Lead workflow discovery, multi-agent orchestration, context and state management, integrations, full-stack interfaces, evaluation, guardrails, testing, monitoring, and staged releases. Measure business outcomes and determine when agents, simpler automation, or no solution is appropriate. The role requires ownership of agent systems from architecture through production improvement and reuse.

Lead the architecture, development, deployment, and continuous improvement of enterprise AI and machine learning systems. Design generative AI, RAG, agentic, orchestration, MLOps, evaluation, monitoring, and security patterns. Build AI-enabled workflows integrating enterprise data, tools, and model APIs. Partner with executives, engineering, data science, security, and business teams to convert ambiguous problems into scalable solutions and measurable outcomes. Provide technical leadership, mentorship, reusable components, and responsible AI controls.

Build and operate production small language models for real-time AI governance. Responsibilities include model training, fine-tuning, distillation, high-performance inference, synthetic-data generation, evaluation, telemetry, adversarial testing, failure diagnosis, and closed-loop model improvement. The role also provides technical leadership and partners with product, security, research, and customer-facing teams to deploy reliable, low-latency models at enterprise scale.