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Advance detection and measurement in AI systems: expand jailbreak/adversarial libraries; build bias, fairness, and hallucination evaluation methodologies; develop interpretability techniques; translate research into engineering specs and methodology docs; publish work and represent the company with customers, regulators, and at conferences.

Own and design infrastructure architecture for cloud, hybrid, and on-prem AI deployments. Build scalable deployments, CI/CD/GitOps pipelines, observability stacks, and reusable IaC modules. Define SLOs, run incident response and postmortems, optimize performance and cost, ensure security and compliance, and collaborate with ML, backend, and product teams to deliver reliable enterprise AI platform services.

Own end-to-end frontend architecture for AI/DevOps products: build scalable design systems and component libraries, optimize rendering and performance, integrate real-time APIs, ensure security and accessibility, lead releases, mentor junior engineers, and drive frontend best practices across teams.

Design, train, and deploy NLP and ML models for classification, semantic scoring, and safety. Build containerized inference services, optimize CPU-only deployments, implement evaluation and observability pipelines, and integrate ML outputs with backend systems. Contribute to testing, red-teaming, and hybrid ML-rule systems to ensure reliable production AI.

Design, build, test, and maintain scalable backend services and APIs. Participate in architecture (LLD/HLD), debug production issues, implement observability and performance improvements, deliver features end-to-end, perform code reviews, and mentor junior engineers.