[Remote] Applied AI Engineer at Cherre

Remote

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Real Estate Data PlatformIndustries

Skills

Key technologies and capabilities for this role

Large Language Models (LLMs)multi-agent systemsretrieval-augmented frameworksintelligent agentstool usememory/state managementorchestrated LLM workflowscontext-aware LLM workflowsAI pipelinesLangGraphCrewAIn8nLangChainRAGGraph-RAGfine-tuned LLM solutionsreal estate data normalizationreal estate data enrichmentreal estate data summarizationreal estate data analyticsagent patternsreasoning over toolscontext retrievalgoal persistencemulti-step reasoningtool executionproduction systemsagent evaluationagent debuggingLangFuseOpenTelemetrycustom tracesmemory systemsvector searchknowledge graph integrationdynamic personalizationlogic-based chainingagent simulation testingModular Control Plan (MCP)

Questions & Answers

Common questions about this position

What experience is required for the Applied AI Engineer role?

Must-have experience includes 1–3 years in applied ML or LLM research or engineering, building agentic systems using tools like LangGraph, CrewAI, n8n or LangChain, deep familiarity with RAG, Graph-RAG, vector stores, and dynamic tool use, strong Python proficiency with GCP, SQL, and DBT, foundation in statistics, and experience applying NLP and transformer-based models in production.

What is the salary for this position?

This information is not specified in the job description.

Is this role remote or does it require office work?

This information is not specified in the job description.

What does the team collaboration look like at Cherre?

You'll collaborate with engineers, product managers, and domain experts to turn exploratory POCs into robust production systems.

What makes a strong candidate for this Applied AI Engineer position?

A strong candidate has hands-on experience building and deploying intelligent agents with frameworks like LangGraph or LangChain, expertise in RAG and LLM workflows, and proficiency in Python, GCP, SQL, plus a foundation in statistics; pluses include real estate domain experience or LangFuse usage.

Cherre

Real estate data integration platform using AI

About Cherre

Cherre provides a platform that integrates and connects real estate data for organizations, primarily serving real estate investors, managers, and underwriters. The platform uses artificial intelligence to filter and organize data, making it accessible and easy to understand. This data helps clients optimize their investment, management, and underwriting decisions, particularly benefiting Single Family Residential investors with tailored data collections. Cherre operates on a business-to-business model, generating revenue by offering data integration services. Its platform acts as a 'single source of truth', ensuring that all levels of an organization can access accurate data for informed decision-making. Additionally, Cherre's Data Partner Network allows for the seamless addition of new data, keeping clients updated with the most comprehensive information available.

New York City, New YorkHeadquarters

2016Year Founded

$102.1MTotal Funding

SERIES_CCompany Stage

AI & Machine Learning, Financial Services, Real EstateIndustries

51-200Employees