Description

Key Responsibilities:
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 LangChainIntegrate 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.| LangChain| LangGraph| AutoGen)
Strong system design skills with experience building and scaling cloud-based applications

Key Skills

Education

Bachelor's degree