Associate Principal - Architecture

Key Responsibilities

Experience: 12-16 Years
Role: AI Architect – Agentic AI & Advanced RAG
Employment Type: Full-Time

We are looking for an experiencedAI 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.

Technical Expertise

• Strong hands-on development experience in Python.
• Deep expertise in at least two of the following frameworks:
  • 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

• Hands-on experience with vector databases such as:
  • Pinecone
  • FAISS
  • ChromaDB
  • Similar vector search platforms
• Strong understanding of:
  • MCP (Model Context Protocol)
  • Knowledge Graphs
  • Agent memory architectures
  • Tool calling and function-calling mechanisms

Evaluation & Observability

• Experience implementing AI evaluation frameworks such as:
  • RAGAS
  • DeepEval
  • LangSmith Evaluations
• Experience with AI observability and LLMOps platforms including:
  • 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.