Director, AI Engineering (Data Science)
Columbia, MD, United States
USD 203K-262K (estimate) Executive-level Full Time
Tasks
- Build robust data pipelines
- Containerize and orchestrate ML services
- Define AI roadmap
- Design and develop custom transformer models
- Drive attention mechanisms positional encodings and tokenization
- Ensure experimental design statistical rigor and causal inference
- Establish code review and model governance
- Establish production standards for deployment monitoring and drift detection
- Fine tune foundation models for client datasets
- Implement ML CI CD model retraining
- Implement experiment tracking
- Implement model governance explainability and responsible AI standards
- Lead A B testing and offline online evaluation
- Lead technical discovery and solutioning with clients
- Mentor and manage AI ML engineers
- Oversee data science workflows feature engineering model validation deployment
- Own AI model development lifecycle
- Present AI strategy and results to executives
- Set technical strategy and engineering best practices
- Support RFP responses and technical proposal work
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWS SageMaker | Automated retraining | Azure Machine Learning | B testing | BERT | BigQuery | CI/CD | Causal Inference | DVC | Databricks | Deep learning | Docker | Drift Detection | Explainable AI | GCP Vertex | GCP Vertex AI | HuggingFace Transformers | Kubernetes | LightGBM | MLOps | MLflow | Machine Learning | Model Governance | Monitoring | PyTorch | Python | Responsible AI | SQL | Scikit-learn | Snowflake | Spark | T5 | TensorFlow | Transformer | Vertex AI | Weights and Biases | XGBoost
Education
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