Principle AI engineer

Urbane Systems; New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

Urbane Systems in New York seeks an experienced software engineer to advance large-scale Python systems, APIs, microservices, and data pipelines in production.

You will work on agentic AI initiatives with LangGraph, Google ADK, CrewAI, Claude Agent SDK, building MCP servers and tool-calling interfaces.

Candidates should have deep knowledge of data structures and algorithms, system design, and production-grade distributed systems, with 8–14 years of software engineering experience.

Qualifications

• 8–14 years of software engineering experience with hands-on Python
• Experience with at least one systems/backend language (Go, Rust, Java, or C/C++) and the judgment to know when to reach for it
• Strong data structures and algorithms
• Deep understanding of APIs, microservices and system design
• Hands-on experience building and operating data pipelines and production-grade distributed systems
• 2+ years of hands-on LLM engineering
• Production experience with agent frameworks and MCP servers and tool-calling interfaces

Skills

8-14 years of software engineering

Large-scale Python development

Strong data structures and algorithms

APIs, microservices, and system design

Production-grade distributed systems

LLM engineering

Agent frameworks experience

Tools

LangGraph

Google ADK

CrewAI

Claude Agent SDK

MCP servers

Vector databases (Milvus, Pinecone, Weaviate, FAISS)

Job description

• 8-14 years of software engineering experience, with strong hands-on large-scale Python
• Working depth in at least one systems or backend language - Go, Rust, Java, or C/C++ - and the judgment to know when to reach for it
• Strong data structures and algorithms.
• Strong understanding of APIs, microservices, and system design
• Hands-on experience building and operating data pipelines and production-grade distributed systems.

Agentic AI and LLMs

• 2+ years of hands-on LLM engineering, with at least couple agentic system you designed and took to production
• Production experience with agent frameworks - LangGraph, Google ADK, CrewAI, Claude Agent SDK, or equivalent - and the fluency to move between them as the ecosystem evolves
• Experience building MCP (Model Context Protocol) servers and tool-calling interfaces
• RAG from first principles: chunking strategy, embeddings, vector and hybrid retrieval, reranking, and response validation
• Strong experience with vector databases (Milvus, Pinecone, Weaviate, FAISS, etc. or cloud equivalents)
• Design of guardrails and reliability patterns - validators, policy checks, self-correction loops, deterministic fallbacks, circuit breakers, and rollback paths