Lead the design, development, and production deployment of autonomous multi-agent systems
Design multi-agent architectures capable of breaking down complex user queries into sub-tasks
Define the state management strategy
Architect robust Retrieval-Augmented Generation (RAG) pipelines
Implement tool-use capabilities, enabling agents to interact with internal APIs, databases, and third-party platforms safely
Develop guardrails and steering mechanisms
Optimize prompt engineering strategies for maximum reliability and minimum latency
Oversee the transition from prototype to production
Implement evaluation frameworks to quantitatively measure agent performance
Design observability dashboards to trace agent reasoning steps in real-time
Manage cost and performance trade-offs, implementing caching strategies

Expert proficiency in Python
Familiarity with TypeScript is a plus
Deep experience with LangChain and specifically agentic libraries like LangGraph, AutoGen, or Semantic Kernel
Experience deploying and managing vector stores like Pinecone, Weaviate, Qdrant, or pgvector
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

AI Architect

Key skills