AI ARCHITECT - Role Overview

We are seeking an experienced AI Architect to lead the design, development, and production deployment of autonomous multi-agent systems. You will move beyond simple chatbots to build stateful, goal-oriented agentic workflows that can reliably execute complex business logic.

Key Responsibilities

Architecture & System Design

• Design multi-agent architectures (e.g., Supervisor-Worker, Hierarchical Teams) capable of breaking down complex user queries into sub-tasks.
• Define the state management strategy to ensure agents retain context, memory, and user intent across long-running workflows.
• Architect robust Retrieval-Augmented Generation (RAG) pipelines that allow agents to query proprietary data with high precision.
• Select and integrate appropriate LLM orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI) based on use-case requirements.

Engineering & Development

• Implement tool-use capabilities (function calling), enabling agents to interact with internal APIs, databases, and third-party SaaS platforms safely.
• Develop guardrails and steering mechanisms (e.g., NeMo Guardrails, LMQL) to ensure agents stay "on-rails" and avoid hallucinations or unsafe actions.
• Optimize prompt engineering strategies (Chain-of-Thought, ReAct, Tree of Thoughts) for maximum reliability and minimum latency.
• Oversee the transition from prototype to production, ensuring code is modular, testable, and scalable.

Production Operations (LLMOps)

• Implement evaluation frameworks (e.g., Ragas, TruLens, DeepEval) to quantitatively measure agent performance, accuracy, and hallucination rates before deployment.
• Design observability dashboards (using tools like LangSmith, Arize, or Datadog) to trace agent reasoning steps, token usage, and latency in real-time.
• Manage cost and performance trade-offs, implementing caching strategies and selecting the right model mix (e.g., routing simpler tasks to smaller/cheaper models like GPT-4o-mini or Llama 3).

Technical Qualifications

Core Tech Stack

• Languages: Expert proficiency in Python; familiarity with TypeScript is a plus.
• LLM Frameworks: Deep experience with LangChain and specifically agentic libraries like LangGraph, AutoGen, or Semantic Kernel.
• Vector Databases: Experience deploying and managing vector stores like Pinecone, Weaviate, Qdrant, or pgvector.
• Model APIs: Hands-on experience integrating OpenAI (GPT-4), Anthropic (Claude), and open-source models (via Ollama or vLLM).

Infrastructure & DevOps

• Experience containerizing AI applications (Docker, Kubernetes) for cloud deployment (AWS/Azure/GCP).
• Familiarity with serverless architectures for handling asynchronous agent tasks.
• Knowledge of API security standards (OAuth, API Keys) for securing agent tool access.

Nice-to-Haves (The "Edge")

• Experience fine-tuning small language models (SLMs) for specific domain tasks to reduce costs and improve latency.
• Background in Graph RAG (using Knowledge Graphs alongside Vector DBs) for better reasoning capabilities.
• Experience dealing with structured outputs (using Pydantic/Instructor) to force LLMs to return valid JSON/Schematic data.