Job description
- 5+ years in software/ML infrastructure engineering
- Build full-stack internal GenAI tools (Python, FastAPI, Streamlit) that let non-engineering teams 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.
- Build and maintain data/ML pipelines (Airflow, Spark, Hive, Trino) supporting multiple business units.
- Partner with product and engineering leads to turn ambiguous problems into shipped features with measurable efficiency gains.
Skills: Data Engineering, Python PySpark, Machine Learning, API, Streamlit
Rate:
Not specified
Location:
United States
IR35 Status:
Outside
Remote Status:
Remote
Industry:
AI & Machine Learning
Seniority Level:
Senior