Required Qualifications & Technical Skills
- Professional Experience: 5+ years of software engineering experience in Python with strong expertise in high-throughput asynchronous services (`asyncio`, FastAPI).
- LLM & Agent Systems: 2+ years of hands-on experience building production LLM applications, agentic workflows, function calling, tool use, and multi-agent interaction protocols (e.g., LangChain, LangGraph, LlamaIndex, or custom frameworks).
- Protocols & Architectures: Solid understanding of Model Context Protocol (MCP), RESTful API design, Agent-to-Agent (A2A) orchestration, and Knowledge Graph/RAG patterns.
- Evaluation Methodologies: Demonstrated experience establishing automated LLM evaluation datasets, synthetic test benches, and scoring metrics for intent detection and tool selection.
- Cloud & Data Stack: Experience with AWS cloud infrastructure (ECS/EKS, Lambda, SQS/Kafka), vector databases, and enterprise data warehouses (Snowflake).
- Software Craftsmanship: Strong commitment to unit/integration testing, CI/CD automation, clear API documentation, and agile team collaboration.
Preferred Experience
Prior experience working with enterprise SaaS, tax/compliance technology, or complex multi-step workflow automation.
Hands-on experience with streaming architectures, semantic caching, and LLM latency optimization.
Familiarity with IAM/RBAC entitlement models and enterprise data security standards (SOC 2, ISO).
Must Haves
Deep experience in Python and high-performance asynchronous API frameworks (asyncio, FastAPI)
Hands-on experience developing LLM agents, tool calling, and multi-agent orchestration
(LangChain, LangGraph, or custom agent architectures)
Experience implementing Model Context Protocol (MCP) or Agent-to-Agent (A2A) integration layers
Experience with LLM evaluation pipelines, synthetic dataset creation, and latency/token optimization