Description

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 LangChain or LangGraph
• Integrate AI systems with APIs, backend services, and cloud platforms
• Establish evaluation, reliability, and performance strategies (accuracy, latency, cost)

Requirements:

• 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
• Strong programming experience in Python, Javascript.
• Strong exp with LangGraph, LangChain, RAG architectures, embeddings, and vector databases.
• Strong exp with AI systems with APIs, backend services, and cloud platforms.
• Years of Experience: 8.00 Years of Experience

Key Skills

Education

Any Graduate