AI Architect

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.

• 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\).

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).

• 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.

• 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.

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