Senior AI Engineer

Key Responsibilities Design, develop, and deploy multi-agent AI systems using open-source or enterprise-level frameworks. Translate complex business requirements into scalable agent-based AI architectures. Implement Generative AI and RAG pipelines, leveraging enterprise-grade tools such as LangChain, LlamaIndex, or similar. Architect and build modular and extensible agentic AI systems, using frameworks like LangGraph, CrewAI, AutoGen, etc. Collaborate with cross-functional teams (Product, Data, Engineering) to ship AI-powered features into production. Build APIs, microservices, or full-stack solutions (as needed) to support AI capabilities. Stay current on the latest advancements in LLMs, multi-agent coordination, and open-source AI tools. Ensure system reliability, scalability, and performance in a production-grade enterprise environment. Required Skills Qualifications Proficient in Python with strong software engineering fundamentals (OOP, design patterns, unit testing, CI/CD). Demonstrated experience building production-ready Generative AI applications (LLMs, RAG, chat-based tools). Hands-on knowledge of LangChain, LlamaIndex, or similar enterprise-grade GenAI libraries. Deep understanding of agent-based AI architectures and frameworks like LangGraph, CrewAI, AutoGen, etc. Experience with multi-agent orchestration, task decomposition, memory management, and reasoning loops. Familiarity with vector databases, embedding models, and prompt engineering best practices. Ability to take business requirements and build intelligent agentic systems with measurable results. Strong understanding of REST APIs, cloud platforms (AWS/GCP/Azure), and optionally DevOps practices. Nice to Have Experience with graph algorithms and graph-based reasoning systems. Familiarity with cognitive architectures or planning algorithms. Contributions to open-source AI projects or research publications.