1.Master's degree from an accredited college or university in Computer Science, Engineering, or related discipline; and eight (8) years of experience in software/AI engineering, four (4) years of which must have been in a leadership role managing cross-functional technical teams such as AI/ML engineers, data scientists, and Machine Learning professionals, and with a proven track records of delivering enterprise-grade AI solutions; or
2. A satisfactorily equivalent combination of education, training, and experience. However, all candidates must have a minimum of a Bachelor’s Degree in disciplines listed in “1” above, or in a related discipline, and preference will be given to applicants with a doctorate degree.
Preferred Certifications:
1. Google Cloud Professional Machine Learning Engineer.
2. AWS Certified Machine Learning – Specialty.
3. Microsoft Certified: Azure AI Engineer Associate.
4. Certified Kubernetes Administrator.
5. TensorFlow Developer Certificate.
6. HL7 FHIR Proficiency Certification.
7. Databricks Certified Professional Data Engineer.
Preferred Knowledge Areas, Skills, Abilities, and other Qualifications:
1. Proven expertise in machine learning, deep learning, generative AI, and AI system design and deployment.
2. Proficient in Python, TensorFlow/PyTorch, cloud services (AWS, Azure, GCP), and MLOps tools.
3. Strong knowledge of Machine Learning practices, including model versioning, CI/CD for Machine Learning, and production monitoring.
4. Experience deploying large-scale AI systems in production environments.
5. Programming Languages & Frameworks: Python/R, TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, LightGBM.
6. Data Engineering & Big Data: SQL and NoSQL databases (e.g., PostgreSQL, MongoDB), Apache Spark, Databricks, Airflow.
7. Cloud Platforms: AWS (e.g., SageMaker, EC2, S3), Microsoft Azure (e.g., Azure ML, Databricks), Google Cloud Platform (e.g., Vertex AI, BigQuery).
8. Machine Learning & Deployment: Docker, Kubernetes, MLflow, Kubeflow, CI/CD tools (GitHub Actions, Jenkins, GitLab CI).
9. Version Control & Collaboration: Git, GitHub, GitLab, JIRA, Confluence.
10. Visualization & BI Tools: Tableau, Power BI, Looker, Jupyter Notebooks, VS Code, PyCharm.
11. APIs & Integration: FastAPI, Flask, and tools for secure model deployment and EHR system integration (where applicable).
12. Proven track record of academic engagement, including collaborations, publications, or conference presentations in AI/ML fields.
Equipment/Machines and Software Operated:
1.General office equipment (e.g., computer, phones, scanner, copier)
