(Apply Now) AI Architect- Agentic AI & RAG

Job Overview:
We are looking for an experienced AI Architect to lead solution design and development efforts in the areas of Agentic AI and Retrieval-Augmented Generation (RAG).

The ideal candidate will have strong experience working with large language models (LLMs), building intelligent applications using frameworks like LangChain or LlamaIndex, and designing scalable AI pipelines.
Key Responsibilities:
- Design and implement RAG-based systems combining LLMs with vector databases and internal document sources.
- Build task-based AI agents (e.g., chatbots, assistants) that can perform multi-step operations and integrate with external tools or APIs.
- Develop and maintain prompt templates, embedding pipelines, and orchestrators using frameworks like LangChain, LlamaIndex, etc.
- Work with data and ML teams to connect AI applications with enterprise knowledge sources (SharePoint, Confluence, databases, etc.).
- Evaluate and integrate embedding models, vector databases (FAISS, Pinecone, Chroma), and LLM APIs (OpenAI, Azure OpenAI, etc.).
- Collaborate with cross-functional teams to identify use cases, define architecture, and deliver PoCs or MVPs.
- Contribute to CoE documentation, reusable components, and architecture patterns for RAG and agent-based solutions.
Required Skills:
- 5 years of hands-on experience in Python with exposure to building AI/NLP applications.
- Practical experience with LLM tools like LangChain, LlamaIndex, or equivalent.
- Experience building RAG systems using vector stores and document loaders.
- Familiarity with LLM APIs such as OpenAI, Cohere, or HuggingFace models.
- Understanding of prompt design, embedding models (e.g., OpenAI, BGE, SentenceTransformers).
- Working knowledge of REST APIs, JSON, and integrating AI agents with backend systems.