Machine Learning Engineer Principal
About The Position
SAIC is seeking an AI/Machine Learning Engineer to support the Enterprise Center of Excellence (ECOE) in developing scalable, data-driven solutions for mission-critical aerospace and defense systems. This role focuses on building applied machine learning capabilities on top of structured, governed data systems, enabling predictive analytics, anomaly detection, and decision support at the aircraft and system level. The position bridges data engineering, analytics, and machine learning, supporting the transition from file-based data processing to enterprise data warehouse-driven intelligence.
Requirements
- API ingestion, transformation, and loading into structured data stores
- Integrate machine learning models into production data workflows
- Cloud-based data platforms (AWS, S3, RDS/PostgreSQL)
- Develop and evaluate machine learning models for prediction, classification, and anomaly detection.
- Apply statistical analysis and NLP techniques to structured and semi-structured datasets
- Feature engineering aligned with enterprise data models
- Data integrity, traceability, and validation across pipelines and models
- Automate data validation and testing processes using Python and SQL
- Governed data frameworks and reproducible analytics
- AWS services (S3, EC2, Athena) and/or Google Cloud (BigQuery)
- Distributed data processing tools (e.g., Spark)
- Scalable data architectures for analytics and ML workloads
- Collaborate with engineers, data scientists, and domain experts
- Present analytical findings and model outputs to technical and non-technical stakeholders
- Support customer-facing discussions and solution development
Responsibilities
- Develop and maintain end-to-end data pipelines (API ingestion, transformation, and loading into structured data stores)
- Integrate machine learning models into production data workflows
- Work with cloud-based data platforms (AWS, S3, RDS/PostgreSQL)
- Develop and evaluate machine learning models for prediction, classification, and anomaly detection.
- Apply statistical analysis and NLP techniques to structured and semi-structured datasets
- Support feature engineering aligned with enterprise data models
- Ensure data integrity, traceability, and validation across pipelines and models
- Automate data validation and testing processes using Python and SQL
- Support governed data frameworks and reproducible analytics
- Work with AWS services (S3, EC2, Athena) and/or Google Cloud (BigQuery)
- Utilize distributed data processing tools (e.g., Spark) where applicable
- Support scalable data architectures for analytics and ML workloads
- Collaborate with engineers, data scientists, and domain experts
- Present analytical findings and model outputs to technical and non-technical stakeholders
- Support customer-facing discussions and solution development
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What This Job Offers
Job Type
Full-time
Career Level
Principal
Education Level
No Education Listed
Number of Employees
1-10 employees
