About the Role :

We are seeking a highly experienced Principal Architect to define and drive the architecture strategy for enterprise-scale AI, Generative AI, and Agentic AI initiatives. The ideal candidate will bring deep expertise in Python, Large Language Models (LLMs), RAG, Agentic AI, AI application architecture, and modern AI frameworks.

Key Responsibilities :

- Define enterprise-level architecture and technology strategy for Generative AI, LLM, RAG, and Agentic AI initiatives.

- Architect large-scale AI platforms and applications that can support complex enterprise workflows, intelligent automation, and business use cases.

- Establish reference architectures, design principles, reusable patterns, technology standards, and engineering best practices for AI solutions.

- Lead the architecture of advanced Agentic AI systems involving autonomous agents, tool calling, workflow orchestration, reasoning, memory, and multi-step task execution.

- Define enterprise RAG architectures covering data ingestion, document processing, embeddings, vector search, retrieval, reranking, context management, and LLM generation.

- Evaluate and select LLMs, foundation models, AI frameworks, vector databases, and supporting technologies based on scalability, performance, cost, security, and business requirements.

- Provide architectural leadership across multiple AI/ML engineering teams and mentor Senior Architects, Lead Engineers, and technical leaders.

- Establish standards for prompt engineering, context engineering, model evaluation, fine-tuning, AI application development, and responsible AI implementation.

Required Skills :

- 17 - 25 years of overall experience in software engineering, technology architecture, AI/ML engineering, or related technology leadership roles.

- Strong proficiency in Python and experience architecting production-grade AI applications and platforms.

- Deep hands-on and architectural expertise in Generative AI, LLMs, Agentic AI, and LLM-based architectures.

- Extensive experience with RAG architectures, vector databases, embeddings, semantic search, and knowledge retrieval systems.

- Strong experience with AI/ML frameworks such as PyTorch, TensorFlow, LangChain, LangGraph, LlamaIndex, or equivalent technologies.

- Strong understanding of distributed systems, scalable application architecture, APIs, integration patterns, performance engineering, and reliability.

- Strong understanding of AI security, data privacy, responsible AI, and enterprise AI governance.

- Excellent communication skills with the ability to influence senior technical and non-technical stakeholders.