Agentic AI Engineer

What you'll do

• Build and integrate generative AI applications using pre-trained foundation models via managed APIs and open-source endpoints.
• Design context engineering workflows including system instructions, retrieved knowledge, tool definitions, and task metadata for reliable prompting.
• Develop and maintain Retrieval-Augmented Generation (RAG) systems featuring chunking, embedding, hybrid search, and re-ranking.
• Build knowledge graphs and Graph RAG pipelines to support multi-hop reasoning and grounded responses.
• Create agentic workflows using frameworks like LangGraph or CrewAI to enable tool-calling, structured planning, and memory.
• Integrate agents with external data sources using the Model Context Protocol (MCP) and Agent2Agent protocols.
• Deploy, monitor, and maintain production-grade GenAI applications while ensuring compliance with safety guardrails.
• Perform data preprocessing, document ingestion, and API development to support AI application functionality.

What we're looking for

• Bachelor's degree or Master's degree in Computer Science, Data Science, AI, or a related field.
• 3–5 years of professional experience in software or AI development with hands-on exposure to Generative AI and agentic AI.
• Proficiency in Python for GenAI development, data preprocessing, and scripting.
• Experience building RAG systems including chunking strategies, vector databases, and hybrid search techniques.
• Experience with prompt engineering, context engineering, and consuming major GenAI APIs like OpenAI, Gemini, or Claude.
• Exposure to agentic AI development using frameworks such as Google ADK, LangGraph, CrewAI, or the OpenAI Agents SDK.
• Knowledge of agent tooling, including tool/function calling, Model Context Protocol (MCP), and session management.
• Understanding of containerization with Docker, version control with Git, and responsible AI principles.