Lead Graph AI/ML & Data Engineer

  • Charlotte, NC
  • Posted 1 day ago | Updated 1 day ago

Overview

On Site
$30 - $40
Contract - Independent
Contract - W2
Contract - 12 Month(s)
10% Travel

Skills

XGBoost
PySpark
AWS
GCP

Job Details

Title: Lead Graph AI/ML & Data Engineering

Location: Charlotte, NC

Duration: Long Term

Only on W2

We are seeking a highly skilled and experienced Lead Engineer to drive the development of our Graph-based AI/ML Platform and XGBoost, along with Strong experience in Data Engineering using PySpark. This Involves leading the design and implementation of teams to deliver intelligent graph- driven solutions.

Key Responsibilities:

  • Leading the architecture and development of scalable graph data platforms, designing and implementing Graph Neural Network (GNN) models, optimizing data pipelines with PySpark, and integrating graph ML solutions into production.
  • Required Skills: Minimum 8+ years of experience in Python with expertise in PyG, DGL, XGBoost, Graph Theory, GNN architectures, graph embeddings, PySpark, and distributed data processing.
  • Database & Cloud Experience: Experience with Graph Databases (e.g., Neo4j, TigerGraph, Amazon Neptune) and familiarity with cloud platforms (AWS, Google Cloud Platform, Azure) and containerization.
  • Role Focus: This position involves leading design and implementation teams to deliver intelligent graph-driven solutions, emphasizing strong skills in Graph-based AI/ML and Data Engineering using PySpark.

Required Skills & Qualifications:

  • Core Skills: Requires 8+ years of Python experience, deep understanding of Graph Theory, GNNs, and graph embeddings, along with experience in PySpark and distributed data processing.
  • Data & Cloud Expertise: Solid understanding of data engineering principles (ETL, data modeling), experience with Graph Databases (Neo4j, TigerGraph, Neptune), and familiarity with cloud platforms (AWS, Google Cloud Platform, Azure) and containerization (Docker, Kubernetes).
  • Preferred Qualifications: Experience with MLOps, deploying graph ML models in production, knowledge of streaming data platforms (Kafka, Spark Streaming), and contributions to open-source graph ML libraries or research.
  • Education: Bachelor's or Master's degree in Computer Science, Data Science, or a related field is required.
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