AI Agents Applied Research/Engineering Lead - Executive Director

Job Responsibilities

• Lead research and deployment of agentic AI systems with multi-step workflows, tool calling, and multi-agent orchestration.
• Fine-tune and optimize LLMs using parameter-efficient fine-tuning (PEFT), distillation, and quantization to meet production constraints such as latency, memory, and cost.
• Apply reinforcement learning and preference optimization to improve personalization and dialogue policies.
• Scale LLM systems through caching, batching, prompt governance, and evaluation frameworks.
• Implement privacy, safety, and security controls including PCI compliance, jailbreak resistance, and auditability.
• Design rigorous experiments with strong baselines and meaningful metrics.
• Define and track success metrics for agent performance, including task completion rate, accuracy, latency, and customer satisfaction.

Required Qualifications, Capabilities, And Skills

• Ph.D. with 8+ years or M.S. with 12+ years building and deploying AI systems in production.
• Applied GenAI experience with LLMs including fine-tuning, prompt engineering, and RAG.
• Experience scaling LLM systems with caching, batching, governance, and evaluation.
• Strong foundation in ML, deep learning, statistical modeling, and experimental design.
• Experience in Information Retrieval (indexing, ranking, retrieval) and/or recommendation systems.
• Proficiency in Python and ML frameworks (PyTorch/TensorFlow, Hugging Face, scikit-learn).
• Demonstrated ability to set a technical research agenda and drive it from concept through production deployment.
• Experience presenting research findings and technical strategy to senior leadership and non-technical stakeholders.

Preferred Qualifications, Capabilities, And Skills

• 5+ years developing conversational AI systems, virtual assistants or LLM-based systems in production.
• Experience with multi-agent orchestration, supervisor agents, and specialized toolkits.
• Expertise in agent governance, red-teaming, adversarial testing, and safety evaluation.
• Experience with reinforcement learning, bandit algorithms, and preference-based optimization (DPO, IPO), with practical exposure to data collection, labeling, and evaluation pipelines.
• MLOps/LLMOps experience with CI/CD, monitoring, versioning, A/B testing, and rollbacks.
• Track record of data-driven product development and experimentation.
• PUBLICATIONS IN TOP-TIER AI/ML VENUES AND/OR OPEN-SOURCE CONTRIBUTIONS.