GenAI Data Engineer — Build Scalable AI Tools in Charlotte

Machine Learning Technologies, LLC; Charlotte (NC); On-site; USD 96,000 - 124,000; Full time

Job summary

Machine Learning Technologies, LLC in Charlotte, NC seeks an Agentic AI Data Engineer to build and scale internal GenAI tooling, data pipelines, and observability stacks. You will collaborate with product and engineering to translate ambiguous problems into shipped features with measurable efficiency gains.

The role emphasizes Python, FastAPI, Streamlit, Spark, Airflow, and large-scale data migrations, with a strong focus on security, identity controls, and compliant workflows across multiple

Qualifications

• 5+ years in software/ML infrastructure engineering.
• Build full-stack internal GenAI tools (Python, FastAPI, Streamlit) for non-engineering teams to run LLM analysis over sensitive data, including scalable PII-masking pipelines.
• Design and maintain MCP servers/tools enabling teams to run workflows directly from AI chat clients with strict identity and auth checks.
• Own and evolve labeling platform infrastructure (FastAPI, Celery, Redis, Labelbox), including large-scale data migrations, on-call support, and data-loss recovery.
• Migrate observability stacks from legacy tools to OpenTelemetry, building dashboards and ing as code.

Responsibilities

• Run and evolve GenAI tooling to empower internal teams.
• Maintain data/ML pipelines (Airflow, Spark, Hive, Trino) across business units.
• Collaborate with product and engineering leads to ship features with measurable efficiency gains.

Skills

Data Engineering

Python PySpark

Machine Learning

API

Streamlit

Tools

OpenTelemetry

Airflow

Spark

Hive

Trino

Labelbox

Job description

Machine Learning Technologies, LLC in Charlotte, NC seeks an Agentic AI Data Engineer to build and scale internal GenAI tooling, data pipelines, and observability stacks. You will collaborate with product and engineering to translate ambiguous problems into shipped features with measurable efficiency gains.

The role emphasizes Python, FastAPI, Streamlit, Spark, Airflow, and large-scale data migrations, with a strong focus on security, identity controls, and compliant workflows across multiple