Design, build, and deploy end-to-end machine learning models using proprietary datasets
Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation
Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities
Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices
Package and expose trained models as scalable APIs for consumption by AI Engineers and downstream systems
Build and maintain model pipelines, including versioning, retraining, and continuous improvement workflows
Monitor model performance in production, ensuring accuracy, reliability, and relevance over time
Troubleshoot model drift, data quality issues, and performance bottlenecks

Strong experience in machine learning engineering, with production deployment experience
Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
Experience with data preprocessing, feature engineering, and model evaluation techniques
Hands-on experience deploying ML models as APIs or microservices
Familiarity with MLOps practices, including model versioning, monitoring, and retraining pipelines
Experience working with large-scale or proprietary datasets
Strong software engineering fundamentals (testing, scalability, performance optimization)
Ability to collaborate effectively with cross-functional teams

Accommodations within reason due to a disability or medical need are available on request

Blanc Labs

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