Agentic AI, Gen AI

Job Description -Agentic AI & Generative AI Engineer Experience: 2-9 Years Job Summary We are looking for an innovative Agentic AI & Generative AI Engineer to design, develop, and deploy next-generation AI applications powered by Large Language Models (LLMs), autonomous AI agents, and Generative AI frameworks. The ideal candidate should have hands-on experience building AI copilots, multi-agent systems, RAG-based solutions, and enterprise-grade GenAI applications. Key Responsibilities Design and develop Agentic AI solutions leveraging LLMs and autonomous AI workflows. Build and deploy Generative AI applications including chatbots, copilots, virtual assistants, and knowledge assistants. Develop multi-agent orchestration frameworks for solving complex business problems. Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise data sources. Integrate LLMs with enterprise systems, APIs, databases, and knowledge repositories. Fine-tune, evaluate, and optimize AI models for performance and scalability. Implement prompt engineering, agent planning, memory management, and tool-calling mechanisms. Develop monitoring, observability, security, and governance frameworks for AI solutions. Collaborate with Business Analysts, Product Owners, Architects, and Data Scientists to deliver AI-driven products. Stay updated with emerging GenAI, Agentic AI, and LLM technologies. Job Description -Agentic AI & Generative AI Engineer Experience: 2-9 Years Job Summary We are looking for an innovative Agentic AI & Generative AI Engineer to design, develop, and deploy next-generation AI applications powered by Large Language Models (LLMs), autonomous AI agents, and Generative AI frameworks. The ideal candidate should have hands-on experience building AI copilots, multi-agent systems, RAG-based solutions, and enterprise-grade GenAI applications. Key Responsibilities Design and develop Agentic AI solutions leveraging LLMs and autonomous AI workflows. Build and deploy Generative AI applications including chatbots, copilots, virtual assistants, and knowledge assistants. Develop multi-agent orchestration frameworks for solving complex business problems. Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise data sources. Integrate LLMs with enterprise systems, APIs, databases, and knowledge repositories. Fine-tune, evaluate, and optimize AI models for performance and scalability. Implement prompt engineering, agent planning, memory management, and tool-calling mechanisms. Develop monitoring, observability, security, and governance frameworks for AI solutions. Collaborate with Business Analysts, Product Owners, Architects, and Data Scientists to deliver AI-driven products. Stay updated with emerging GenAI, Agentic AI, and LLM technologies.