Own end-to-end delivery: Lead the full modeling lifecycle for security scenarios, from data ingestion and curation to training, evaluation, deployment, and monitoring. Problem framing, literature review, model design, offline evaluation, online experimentation, and production deployment. Implement and optimize models: Design and implement privacy-preserving data workflows, including anonymization, templating, synthetic augmentation, and quantitative utility measurement. Develop and maintain fine-tuning and adaptation recipes for transformer models, including parameter-efficient methods and reinforcement learning from human or synthetic feedback. Establish objective benchmarks, metrics, and automated gates for accuracy, robustness, safety, and performance, enabling repeatable model shipping. Productionize AI & ML Collaborate with engineering and product teams to productionize models, harden pipelines, and meet service-level objectives for latency, throughput, and availability. Develop fine-tuning techniques for transformer models and establish benchmarks for accuracy, robustness, and performance to ensure reliable model delivery. Drive MLOps best practices: CI/CD, model registry, feature store, model serving, monitoring/drift. Champion Responsible AI: fairness, explainability, privacy (GDPR/CCPA) and security considerations in model design and deployment. Operational excellence: code quality, tests, observability (logs/metrics/traces), on-call ownership for ML services, and SLA adherence. Collaborate cross‑functionally: write design docs/RFCs, partner with PMs and engineers, and drive execution towards predictable outcomes and timelines.