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Niche GenAI and agent-framework expertise plus senior level narrow applicant pool.
Specialized GenAI/LLM expertise transferable across industries but requires strong ML/AI background.
Explicit 7-12 years plus mandatory GenAI, LLM, cloud, and vector database skills.
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Design, architect, and develop scalable Generative AI applications and agentic AI solutions using frameworks like LangChain and Microsoft Copilot Studio.
Build and maintain backend services, APIs, and data pipelines that power AI-driven enterprise products integrating LLMs with cloud AI platforms and vector databases.
Monitor and optimize AI agent performance and workflows, implementing advanced AI patterns such as RAG, tool/function calling, and human-in-the-loop systems.
7 to 12 years of relevant professional experience in software engineering with a focus on Generative AI and LLM technologies.
Strong proficiency in Python with experience developing production-grade backend applications using frameworks like FastAPI or Flask, plus Docker and asynchronous programming skills.
Hands-on experience with at least two agentic AI frameworks (e.g., LangChain, LangGraph, Google ADK, CrewAI, AutoGen, Microsoft Copilot extensibility).
Experience with cloud AI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI/ADK, and practical knowledge of vector databases for RAG implementations.
Experienced software engineer adept at designing complex AI systems with operational responsibility for reliability, accuracy, and performance of generative AI solutions.
Skilled in integrating multiple AI frameworks and cloud services to deliver scalable enterprise AI applications in collaboration with cross-functional global teams.
Strong system architecture and problem-solving ability focused on implementing advanced AI operational patterns (e.g., Agentic RAG, planning & reflection loops).