Key Responsibilities:Define strategy and roadmap for AI agent systems and LLM applications.Lead multiple teams or large-scale AI initiatives focused on fine-tuning, hybrid model integration, and autonomous agent orchestration.Drive adoption of frameworks like LangGraph, AutoGen, or CrewAI for complex workflows (planning, reasoning, tool use, and memory).Ensure architectural robustness, scalability, and resilience of AI systems in production.Collaborate with executive leadership, product owners, and external stakeholders on technical direction and impact.Establish engineering standards and mentor across levels (LAMs, Managers, Architects).Requirements:Expert in LLM fine-tuning, GPU optimization, and AI agent orchestration.Demonstrated success in designing and deploying LLM systems at scale.Deep understanding of cloud-native architectures (ECS, AKS, Lambda), API design, and MLOps practices.Strong leadership, cross-functional collaboration, and communication skills.Hands-on experience with at least one cloud platform: AWS, Azure, or GCP.Understanding of containerization (Docker) and orchestration (Kubernetes)Knowledge of microservices architecture and API gateways.Nice to Have:Experience managing budgets, vendor relationships, and high-impact cross-team projects.Experience with telephony APIs and platforms such as Twilio, Amazon Connect, or Genesys Cloud.Familiarity with AI safety, human-in-the-loop strategies, and responsible AI practices.