AI Data Engineer: ML Pipelines, LLMs & MLOps

SolomonEdwards; Charlotte (NC); On-site; USD 120,000 - 180,000; Full time

Job summary

SolomonEdwards is seeking an AI Data Engineer to build and maintain the data infrastructure that powers enterprise AI applications and machine learning models. You will bridge software development, data science, and AI to ensure secure, scalable, and high-quality data.

Responsibilities include designing scalable ETL/ELT pipelines, enabling LLM/RAG workflows, implementing vector indexes and knowledge graphs, and enforcing data quality, governance, and MLOps practices in a dynamic client-focused

Qualifications

• Strong Python and SQL programming skills with experience in data pipelines.
• Experience building scalable ETL/ELT pipelines for structured and unstructured data.
• Familiarity with distributed processing and workflow orchestration tools.
• Experience with vector stores and search technologies for AI workloads.
• Knowledge of data governance, lineage, and regulatory compliance.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines for enterprise data.
• Develop foundations for LLMs, RAG workflows, and semantic layers.
• Implement and optimize vector indexes, knowledge graphs, and retrieval systems.
• Establish data quality checks, bias detection, and lineage tracking.
• Deploy models to production with MLOps practices and monitoring.

Skills

Python

SQL

Java/TypeScript

Tools

Apache Spark

Airflow

Dagster

dbt

Pinecone

Elasticsearch/OpenSearch

Milvus

FAISS

Neo4j

AWS Neptune

Job description

SolomonEdwards is seeking an AI Data Engineer to build and maintain the data infrastructure that powers enterprise AI applications and machine learning models. You will bridge software development, data science, and AI to ensure secure, scalable, and high-quality data.

Responsibilities include designing scalable ETL/ELT pipelines, enabling LLM/RAG workflows, implementing vector indexes and knowledge graphs, and enforcing data quality, governance, and MLOps practices in a dynamic client-focused