Associate Principal - Architecture

Key Responsibilities Experience: 12-16 Years Role: AI Architect – Agentic AI & Advanced RAG Employment Type: Full-Time AI Architect – Agentic AI, RAG & We are looking for an experienced AI Architect to lead the design and development of enterprise-scale Agentic AI solutions. The ideal candidate will possess deep expertise in AI architecture, multi-agent systems, advanced RAG implementations, and modern AI platforms. This role will drive the creation of scalable, secure, and production-ready AI solutions while providing technical leadership across AI engineering initiatives. AI Architecture & Solution Design Design and architect enterprise-scale Agentic AI solutions leveraging autonomous and collaborative AI agents. Define end-to-end architecture for AI applications, including LLMs, RAG pipelines, agent orchestration, tool integration, memory management, and workflow automation. Establish architectural patterns and best practices for multi-agent systems, planning agents, reasoning agents, and task orchestration frameworks. Drive AI platform modernization initiatives and define reusable frameworks, accelerators, and reference architectures. Agent Development & Orchestration Design, build, and optimize AI agents using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, Google ADK, or equivalent. Implement complex multi-agent workflows involving planning, reasoning, task decomposition, collaboration, and human-in-the-loop processes. Develop agent communication and orchestration mechanisms using modern agentic architecture principles. Advanced RAG & Knowledge Systems Architect and implement advanced RAG solutions using hybrid search, semantic retrieval, graph-based retrieval, agentic retrieval, and contextual memory. 
 Design scalable knowledge ingestion, indexing, chunking, embedding, and retrieval pipelines. 
 Integrate enterprise knowledge sources including documents, databases, APIs, and knowledge repositories. 
 Leverage Knowledge Graphs to enhance contextual understanding and improve agent reasoning capabilities. 
 
 AI Platform Engineering 
 
 Develop backend services and APIs using Python and FastAPI. 
 Design scalable AI microservices and deploy cloud-native AI solutions. 
 Integrate vector databases such as Pinecone, FAISS, ChromaDB, or equivalent platforms for semantic retrieval. 
 
 AI Evaluation, Monitoring & Governance 
 
 Define evaluation frameworks to measure AI system quality, accuracy, relevance, faithfulness, latency, and business outcomes. 
 Implement AI evaluation methodologies using RAGAS, DeepEval, LangSmith, or similar tools. 
 Establish observability and monitoring practices using solutions such as LangSmith, Langfuse, Arize, or equivalent. Collaborate with governance and security teams to ensure responsible AI implementation and compliance. Technical Leadership Provide technical guidance and mentorship to AI engineers and solution architects. Conduct architecture reviews, code reviews, and design workshops. Collaborate with business stakeholders to translate business requirements into scalable AI solutions. Drive innovation by evaluating emerging trends in Agentic AI, LLMOps, AI infrastructure, and autonomous systems. Required Skills & Technical Expertise Strong hands-on development experience in Python. LangChain LangGraph AutoGen CrewAI Google ADK Strong experience designing and implementing Agentic AI applications and Multi-Agent Architectures. Advanced knowledge of Retrieval-Augmented Generation (RAG) architectures and optimization techniques. Expertise in semantic search, embeddings, retrieval techniques, and vector-based architectures. AI Infrastructure & Platforms Pinecone FAISS ChromaDB Similar vector search platforms MCP (Model Context Protocol) Knowledge Graphs Agent memory architectures Tool calling and function-calling mechanisms Evaluation & Observability RAGAS DeepEval LangSmith Evaluations Experience with AI observability LangSmith Langfuse Arize AI Equivalent monitoring tools API Integration & Development Experience building production-grade APIs using FastAPI. Strong understanding of REST APIs, API integrations, and enterprise application integration patterns. Architecture & Design Experience designing highly scalable, secure, and resilient AI systems. Strong understanding of microservices, distributed systems, and cloud-native architectures. Ability to define reference architectures, design patterns, and reusable AI solution components. Experience leading AI architecture and enterprise AI transformation initiatives. Exposure to cloud platforms such as Azure, AWS, or GCP. Experience with AI governance, security, and responsible AI frameworks. Strong stakeholder management and technical leadership skills.