Senior AI Engineer (HQ or Miami)

Faropoint•Miami, FL

23h•Hybrid

About The Position

Faropoint is a vertically integrated real estate investment manager focused on last-mile industrial assets. We are moving beyond standard automation to build an Enterprise Agentic Platform—a system where AI agents reason over our data to underwrite deals, manage assets, and optimize portfolios autonomously. We are seeking a Senior AI Engineer to build and deploy the semantic foundation and agentic workflows that power this platform. This is a hands-on implementation role—you will be writing production code, deploying AI systems, and establishing the technical patterns that make AI work at institutional scale. While you may help inform architectural direction, your primary focus is building working systems.

Requirements

Engineering Background: 4+ years in software or data engineering, with 1+ years focused on LLM production systems.
Agentic Frameworks: Deep hands-on experience with frameworks like LangChain, LangGraph, or LlamaIndex.
AWS Native: Comfortable building serverless solutions on AWS.
Python Mastery: Expert-level Python skills, ideally with Pydantic for structured data validation and async programming patterns.
Production Mindset: You care about observability, reliability, and cost optimization. You don't ship "black boxes"; you ship systems where every step is traced, logged, and evaluated.

Nice To Haves

Data & Semantics: Experience with knowledge graphs (Neo4j, Neptune), vector databases (Pinecone, pgvector), or semantic layers (Cube, dbt, or custom implementations). Knowledge of Snowflake data platforms is valuable for future work.
GraphRAG Experience: Combining knowledge graphs with LLMs or implementing advanced hybrid retrieval approaches.
Domain Experience: Background in FinTech, PropTech, or quantitative investment firms.
AI Tooling: Experience with AI-assisted development tools (Claude Code, Cursor, Copilot) and observability platforms (LangFuse, Arize, LangSmith).
Infrastructure as Code: Experience with Terraform, CDK, or SAM for managing cloud infrastructure.
Open Source: Contributions to open-source AI libraries or frameworks.

Responsibilities

Build and deploy multi-agent systems using (e.g., LangChain/LangGraph) that handle complex, stateful processes (e.g., "Read this 50-page lease, extract clauses, compare against market data, and flag risks").
Deploy AI solutions using AWS serverless patterns (Step Functions, Lambda, EventBridge, Bedrock) with proper error handling and retry logic.
Implement retrieval-augmented generation systems that combine vector search with structured data queries for accurate, grounded responses.
Build monitoring and evaluation systems using tools like LangFuse to trace agent behavior, track costs, and measure output quality.
Design the ontology and semantic interface that bridges our raw data sources and AI agents, ensuring LLMs understand complex real estate concepts (Cap Rates, NOI, Lease Spreads) without hallucinating.
Build and maintain knowledge graphs that provide agents with structured, context-aware access to proprietary data and market intelligence.
Implement GraphRAG and hybrid search approaches that perform complex reasoning over both structured and unstructured data.
Implement strict guardrails, PII masking, and role-based access control (RBAC) to ensure agents operate safely within financial regulations.

Benefits

High Impact: Build production-grade AI systems that directly influence multi-million dollar investment decisions. Your agents will be the "Analyst of the Future."
Existing Foundation: We've already automated high-impact workflows and saved hundreds of hours per week. You'll be building on proven use cases, not starting from zero.
Modern Stack: Work with modern data infrastructure (Postgres, dbt, AWS) and the latest AI tooling (Bedrock, LangFuse, Claude Code).
Autonomy & Ownership: This is a high-trust role with meaningful technical autonomy. You'll have executive visibility and direct ownership over how AI is implemented across the firm.
Define the Future: Help establish what enterprise agentic AI looks like in practice. Your work will set patterns and standards for how institutional investors use AI.

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