Onsite Agentic AI Data Engineer – Healthcare GenAI Tools

Robotics Technologies; Baltimore (MD); On-site; USD 89,000 - 126,000; Full time

Job summary

Robotics Technologies in Baltimore is hiring for an on-site role focused on building GenAI tooling and data infrastructure for healthcare teams. You will develop full-stack tools using Python, FastAPI, and Streamlit to enable non-engineering teams to run LLM analyses on sensitive data.

You will design scalable PII-masking pipelines, MCP servers, and labeling platform infrastructure with Redis, Labelbox and OpenTelemetry.

Qualifications

• 5+ years of experience in software and/or ML infrastructure engineering.

Responsibilities

• Build full-stack internal GenAI tools using Python, FastAPI, and Streamlit to enable non-engineering teams to run LLM analysis on sensitive data.
• Develop and run scalable PII-masking pipelines for handling sensitive datasets.
• Design and maintain MCP servers and tools that let teams execute workflows directly from AI chat clients.
• Implement and enforce strict identity and authentication checks for chat-driven workflow execution.
• Own and evolve labeling platform infrastructure using FastAPI, Celery, Redis, and Labelbox.
• Support large-scale data migrations for labeling workflows, including on-call support and data-loss recovery.
• Lead migrations of observability from legacy tooling to OpenTelemetry.
• Create dashboards and manage observability via infrastructure-as-code.
• Build and maintain data and ML pipelines across Airflow, Spark, Hive, and Trino for multiple business units.
• Partner with product and engineering leads to convert ambiguous needs into shipped features with measurable efficiency gains.

Skills

Python

FastAPI

Streamlit

Tools

MCP

Celery

Redis

Labelbox

OpenTelemetry

Airflow

Spark

Hive

Trino

Job description

Robotics Technologies in Baltimore is hiring for an on-site role focused on building GenAI tooling and data infrastructure for healthcare teams. You will develop full-stack tools using Python, FastAPI, and Streamlit to enable non-engineering teams to run LLM analyses on sensitive data.

You will design scalable PII-masking pipelines, MCP servers, and labeling platform infrastructure with Redis, Labelbox and OpenTelemetry.