GenAI Data Engineer: Build Scalable ML Infra & Tools

MACHINE LEARNING TECHNOLOGIES LLC; Baltimore (MD); On-site; USD 89,000 - 126,000; Full time

Job summary

MACHINE LEARNING TECHNOLOGIES LLC is seeking an Agentic AI Data Engineer in Charlotte, NC, to build GenAI data tooling and scalable ML pipelines for sensitive healthcare data. You will own labeling infrastructure and data migrations while ensuring strict authentication and data protection.

The role focuses on Python-based toolchains (FastAPI, Streamlit) and modern observability practices, enabling cross-team collaboration and measurable efficiency gains.

Qualifications

• 5+ years in software/ML infrastructure engineering.
• Experience building GenAI tools with Python, FastAPI, Streamlit.
• Experience with PII-masking pipelines and secure data handling.
• Experience maintaining labeling platforms and data migrations.

Responsibilities

• Build full-stack internal GenAI tools (Python, FastAPI, Streamlit) to enable non-engineering teams to run LLM analyses over sensitive data.
• Design and maintain MCP servers/tools with strict identity and auth checks.
• Own and evolve labeling platform infrastructure (FastAPI, Celery, Redis, Labelbox) including data migrations and on-call support.
• Migrate observability stacks to OpenTelemetry and build dashboards as code.
• Build and maintain data/ML pipelines (Airflow, Spark, Hive, Trino) for multiple business units.
• Collaborate with product and engineering leads to turn ambiguous problems into shipped features with measurable efficiency gains.

Skills

Python

PySpark

Machine Learning

API design

Tools

Airflow

Spark

Hive

Trino

Celery

Redis

Labelbox

OpenTelemetry

Job description

MACHINE LEARNING TECHNOLOGIES LLC is seeking an Agentic AI Data Engineer in Charlotte, NC, to build GenAI data tooling and scalable ML pipelines for sensitive healthcare data. You will own labeling infrastructure and data migrations while ensuring strict authentication and data protection.

The role focuses on Python-based toolchains (FastAPI, Streamlit) and modern observability practices, enabling cross-team collaboration and measurable efficiency gains.