ML-Driven Data Engineer for LLM Analytics

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

Benefits offered by this job

Health insurance

Retirement savings plan

Paid holidays

Paid time off

Job summary

Link Logistics Real Estate is hiring 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 will work with investment analysts and asset managers, combining ML and graph-based reasoning with LLM-native workflows, while ensuring data provenance and low-latency APIs for trusted analysis.

Qualifications

• 4+ years in ML engineering or backend engineering
• Experience shipping LLM-powered apps in production (RAG, eval frameworks)
• Strong Python skills and API/backend ownership
• Experience with knowledge graphs or graph databases (Neo4j or similar)
• Proficiency building data pipelines at scale (Spark/Databricks)
• Deep concern for data provenance and trust in analyst outputs

Responsibilities

• Knowledge Graph & Retrieval: design graph-based structures and retrieval pipelines for grounding LLMS
• LLM Workflow Automation: develop rule-based and agentic LLM workflows, prompts, eval
• Data Pipelines: build ETL/ELT workflows for large-scale datasets with owners and SLAs
• Data Trust & Provenance: implement lineage tracking and confidence scoring for derived outputs
• Backend API Layer: build and maintain APIs/services with low latency and clean contracts
• Collaboration: work with investment/asset teams to translate analytical needs

Skills

ML engineering

Backend engineering

Python

Knowledge graphs

Data pipelines

Data provenance

LLM workflows

Tools

Neo4j

Spark

Databricks

Job description

Link Logistics Real Estate is hiring 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 will work with investment analysts and asset managers, combining ML and graph-based reasoning with LLM-native workflows, while ensuring data provenance and low-latency APIs for trusted analysis.