ML-Driven Data Engineer: Knowledge Graph & LLM Workflows

Link Logistics; New York (NY); Hybrid; USD 140,000 - 155,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

Paid holidays

Paid time off

Job summary

Link Logistics Real Estate in New York is seeking a hybrid ML/Backend Engineer to own the intelligence layer of Link's Analytics Engine. You will design data pipelines, build knowledge graphs, and create backend services that surface insights to investment teams in real time.

You'll blend classical ML, graph-based reasoning, and LLM-native workflows to deliver scalable solutions. The role collaborates with analysts and asset managers to close analytical gaps and improve decision making.

Qualifications

• 4+ years in ML engineering or backend engineering.
• Experience shipping LLM-powered apps in production (RAG, prompts, eval frameworks).
• Strong Python skills and ownership of backend services/APIs.
• Experience with knowledge graphs or graph databases (Neo4j).

Responsibilities

• Design graph-based data structures encoding relationships across markets, assets, tenants, and transactions.
• Build retrieval pipelines (RAG, hybrid search, structured queries) for LLM grounding.
• Develop rule-based and agentic LLM workflows; manage prompts and eval frameworks.
• Create ETL/ELT pipelines for large datasets with clear ownership, cadence and SLA.
• Develop and maintain APIs/services exposing ML outputs with low latency and clean contracts.
• Collaborate with investment and asset management teams to translate needs into scalable analytics.

Skills

ML Engineering

LLM Pipelines

Python

Knowledge Graphs

Data Pipelines

Data Provenance

Tools

Neo4j

Spark

Databricks

Job description

Link Logistics Real Estate in New York is seeking a hybrid ML/Backend Engineer to own the intelligence layer of Link's Analytics Engine. You will design data pipelines, build knowledge graphs, and create backend services that surface insights to investment teams in real time.

You'll blend classical ML, graph-based reasoning, and LLM-native workflows to deliver scalable solutions. The role collaborates with analysts and asset managers to close analytical gaps and improve decision making.