Responsibilities: AI Application Development & Deployment: Architect, build, and deploy enterprise-grade AI-powered applications using modern backend technologies (Python, Node.js, FastAPI, Express); Design and implement robust APIs and microservices architectures that integrate AI/ML models-including LLMs and agentic systems-with business systems at scale; Lead containerization strategies using Docker and manage complex deployments via Kubernetes (EKS/ECS) with a focus on reliability, scalability, and performance; Design and implement event-driven architectures using Kafka or similar streaming platforms for real-time data processing and AI inference; Take full ownership of end-to-end delivery from technical scoping and architecture design through production deployment, monitoring, optimization, and ongoing operational excellence. Field Insights & Platform Feedback: Champion continuous improvement by bringing strategic learnings from field deployments back to the core AI/Data and Platform teams; Identify opportunities to improve tools, infrastructure, and reusable components that benefit the broader organization; Author and maintain comprehensive documentation including solution architectures, design patterns, and operational runbooks that enable knowledge transfer and accelerate future deployments.