• Lead Supervised Fine-Tuning (SFT) of large language models in production, shaping instruction-following, reasoning quality, tone, and domain-specific behavior • Extend SFT pipelines with instruction tuning and preference-based optimization (e.g., RLHF-style approaches or direct preference optimization) • Design, curate, and maintain high-quality SFT and preference datasets, combining human-labeled and synthetic data tailored to real-world marketing and decisioning use cases • Own model evaluation and benchmarking, including: • Offline behavioral evals (instruction adherence, reasoning depth, hallucination rates) • Online experiments and A/B tests • Continuous regression detection and performance monitoring • Develop and operate agentic LLM systems, enabling multi-step reasoning, tool use, workflow orchestration, and decision execution • Implement and optimize prompting, retrieval-augmented generation (RAG), memory, and tool-calling strategies, with a clear understanding of when to solve problems via SFT versus prompting • Partner closely with data engineering, platform, and product teams to integrate fine-tuned models into high-throughput, low-latency systems • Establish best practices for LLM versioning, experimentation, deployment, rollback, governance, and safety • Provide technical leadership and mentorship to engineers working on applied AI and LLM systems.