Bebo Technologies - Principal Agentic AI Engineer

Description

• 7-9 years of experience in Python.
• Hands-on experience with LangChain, LangGraph, CrewAI, or similar frameworks.
• Strong foundation in Python and microservices-based architectures.
• Experience with LLMs and agent frameworks.
• Hands-on experience developing and tuning RAG pipelines that rely on embeddings, vector databases, and intelligent retrieval logic.
• Work on RAG-based search including data ingestion and retrieval tuning.
• Evaluate LLMs and agent frameworks for performance, cost, and use case suitability.
  

Job Summary

Key Responsibilities

• Design, develop, and maintain scalable Python-based services for GenAI applications.
• Build and tune Retrieval-Augmented Generation (RAG) pipelines, including data ingestion, embeddings, vector storage, and retrieval logic.
• Implement and optimize agent-based workflows using frameworks such as LangChain, LangGraph, CrewAI, or similar.
• Develop intelligent RAG-based search solutions with optimized retrieval and ranking strategies.
• Evaluate and select LLMs and agent frameworks based on performance, cost, latency, and use-case requirements.
• Architect and implement microservices-based systems for AI workloads.
• Collaborate with product, data, and platform teams to translate requirements into technical solutions.
• Ensure best practices in code quality, system reliability, scalability, and security.
• Conduct performance tuning, monitoring, and troubleshooting of AI pipelines.
  

Required Skills & Qualifications

• 7- 9 years of hands-on experience in Python development.
• Strong foundation in Python programming and microservices architectures.
• Hands-on experience with LangChain, LangGraph, CrewAI, or similar GenAI frameworks.
• Practical experience working with LLMs and agent-based architectures.
• Proven experience developing and tuning RAG pipelines.
• Strong knowledge of embeddings, vector databases, and intelligent retrieval systems.
• Experience in data ingestion pipelines and retrieval optimization.