Architect and deliver end-to-end LLM-powered applications and agentic workflows using Python Design and implement RAG pipelines over enterprise data using embeddings and vector databases Build multi-step, tool-using agents (planning, execution, memory) using frameworks such as LanEligible to workhain Integrate AI systems with APIs, backend services, and cloud platforms Establish evaluation, reliability, and performance strategies (accuracy, latency, cost) Key Qualifications Strong Python expertise with experience building and deploying production-grade backend systems Hands-on experience developing applications using LLMs, including prompt engineering and orchestration Proven experience with RAG architectures, embeddings, and vector databases Experience with agentic frameworks (e.g., LanEligible to workhain, LangGraph, AutoGen) Strong system design skills with experience building and scaling cloud-based applications. This role owns the end-to-end development of intelligent solutions-from architecture to deployment-leveraging Python, modern LLM frameworks, and scalable system design.