PingWind
AI Solution Engineer- Ops
About this role
The AI Solution Engineer at PingWind will support the Golden Dome Supply Chain Enterprise by managing analytics workflows and machine learning operations. The role requires a strong background in data science and proficiency in ETL processes, while working in a hybrid environment between Huntsville, AL and Washington, DC.
What you'll do
- Execute ETL pipelines for data ingestion and transformation.
- Run and monitor ML model inference jobs, identifying issues.
- Automate data pipelines using Python, SQL, and frameworks like Airflow.
- Produce analytics outputs for Red and Blue Team assessments.
- Participate in data quality validation and maintenance tasks.
- Maintain documentation and provenance records per program standards.
What they're looking for
- 4+ years in data science or ML operations
- Proficiency in Python and SQL
- Experience with ETL/ELT processes
- Familiarity with ML model deployment in the cloud
- Strong documentation practices
- Knowledge of data quality validation
- Experience with data pipeline tools (e.g., Airflow, Spark)
- Familiarity with government data handling requirements
Benefits
- Eleven Federal Holidays
- Accrued Paid Time Off
- Parental Leave
- Three medical plan choices
- Dental and Vision Insurance
- 401k with competitive matching
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PingWind
PingWind builds SaaS/PaaS solutions and business intelligence dashboard systems, with work supporting government agencies including the Veterans Benefits Administration. The company is hiring quality assurance engineers, front-end developers, DevSecOps engineers, and business intelligence software engineers for roles emphasizing secure infrastructure, user experience, and data visibility.
View all jobs at PingWindLikely interview questions
- Walk us through your experience building and maintaining ETL/ELT pipelines in production. What tools have you used, and how did you handle data quality issues?
- Describe your experience deploying and monitoring ML models in a cloud environment. How have you detected and responded to model performance degradation?