Applied AI Engineer

Cherre

full-time

Posted on: 9/16/2025

Origin: • 🇺🇸 United States

✨ AI Apply

💰 $100,000 - $120,000 per year

Junior

Google Cloud PlatformOpen SourcePythonSQL

About the role

Design and build AI pipelines using frameworks like LangGraph, CrewAI, n8n, and LangChain to create modular, testable, and composable agents.
Build and scale RAG, Graph-RAG, and custom fine-tuned LLM solutions for real estate data normalization, enrichment, summarization, and analytics.
Develop agent patterns that can reason over tools, retrieve context, and persist goals—bringing multi-step reasoning and tool execution logic to life.
Collaborate with engineers, product managers, and domain experts to turn exploratory POCs into robust production systems.
Contribute to internal frameworks and standards for evaluating and debugging agents (e.g., using LangFuse, OpenTelemetry, or custom traces).
Drive continuous experimentation with memory systems, vector search, and knowledge graph integration for dynamic personalization and logic-based chaining.
Participate in agent simulation testing and contribute to establishing MCP (Modular Control Plan)-based design strategies for safe and reusable AI behaviors.

1–3 years experience in applied ML or LLM research or engineering.
Demonstrated experience building agentic systems using tools like LangGraph, CrewAI, n8n, flowise, or LangChain.
Deep familiarity with RAG, Graph-RAG, vector stores, and dynamic tool use orchestration.
Strong Python proficiency.
Experience with GCP, SQL, and DBT.
Foundation in statistics, including hypothesis testing, regression, and time series analysis.
Demonstrated experience applying NLP and transformer-based models in production workflows.
Real-world use of LangFuse or equivalent frameworks for tracing and observability (preferred).
Prior work in real estate, financial services, or other structured yet messy domains (preferred).
Contributions to open source agent or orchestration libraries (preferred).
Previous experience in developing and deploying LLM-based solutions (preferred).
Exposure to real estate data or a related field (preferred).
Strong analytical and problem-solving skills.