Responsibilities • Design and build scalable end-to-end enterprise systems, bridging advanced frontends, mobile applications, and high-performance async backends. • Deploy, manage, and optimize the production inferencing of large machine learning models (TensorFlow, PyTorch) and Large Language Models (LLMs) within Agentic AI architectures. • Define robust GraphQL integration contracts to seamlessly connect AI outputs with core enterprise services. • Translate ambiguous business needs into concrete technical architectures and actionable engineering tasks. • Lead cross-functional engineering teams through the full delivery lifecycle to drive measurable business ROI. • Manage stakeholder expectations regarding realistic AI capabilities, mitigating risks, and defining strict project boundaries. • Optimize system performance across compute, memory, and network layers, including low-latency LLM inference and cost-efficient resource utilization (GPU/CPU). • Design secure systems with robust authentication/authorization (OAuth, RBAC), data privacy safeguards, and defenses against AI-specific threats such as prompt injection and data exfiltration. • Design and build scalable, event-driven systems that can handle high traffic reliably, using best practices to ensure data consistency and safe processing (e.g., avoiding duplicate or lost events).