Applied AI Engineer

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

Requirements

Candidates should have 1-3 years of experience in applied ML or LLM research or engineering, with demonstrated experience building agentic systems using tools like LangGraph, CrewAI, n8n, flowise, or LangChain, and not just prompt engineering. Deep familiarity with RAG, Graph-RAG, vector stores, and dynamic tool use orchestration is required, along with strong Python proficiency and experience with GCP, SQL, and DBT. A foundation in statistics, including hypothesis testing, regression, and time series analysis, and demonstrated experience applying NLP and transformer-based models in production workflows are also necessary. Experience with LangFuse or equivalent frameworks for tracing and observability of LLM interactions, prior work in real estate or financial services, contributions to open source agent or orchestration libraries, previous experience in developing and deploying LLM-based solutions, and exposure to real estate data or a related field are considered a plus.

Responsibilities

The Applied AI Engineer will design and build AI pipelines using frameworks like LangGraph, CrewAI, n8n, and LangChain to create modular, testable, and composable agents. They will build and scale RAG, Graph-RAG, and custom fine-tuned LLM solutions for real estate data normalization, enrichment, summarization, and analytics. Responsibilities include developing agent patterns that can reason over tools, retrieve context, and persist goals, bringing multi-step reasoning and tool execution logic to life. The engineer will collaborate with cross-functional teams to turn exploratory POCs into robust production systems, contribute to internal frameworks and standards for evaluating and debugging agents, and drive continuous experimentation with memory systems, vector search, and knowledge graph integration. Participation in agent simulation testing and contribution to establishing MCP-based design strategies for safe and reusable AI behaviors are also key duties.

Skills

Large Language Models (LLMs)

multi-agent systems

retrieval-augmented frameworks

intelligent agents

tool use

memory/state management

orchestrated LLM workflows

context-aware LLM workflows

AI pipelines

LangGraph

CrewAI

n8n

LangChain

RAG

Graph-RAG

fine-tuned LLM solutions

real estate data normalization

real estate data enrichment

real estate data summarization

real estate data analytics

agent patterns

reasoning over tools

context retrieval

goal persistence

multi-step reasoning

tool execution

production systems

agent evaluation

agent debugging

LangFuse

OpenTelemetry

custom traces

memory systems

vector search

knowledge graph integration

dynamic personalization

logic-based chaining

agent simulation testing

Modular Control Plan (MCP)

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