Job Description
- Design, develop, and implement machine learning and deep learning models.
- Preprocess and analyze large-scale structured and unstructured datasets.
- Optimize models for performance, scalability, and efficiency.
- Integrate models into production systems using APIs or cloud-based deployment.
- Monitor, test, and retrain models as required based on feedback and performance.
- Document model development, architecture, and performance metrics.
- Design scalable infrastructure for training, deploying, and monitoring ML and LLM models in production.
- Manage Azure Kubernetes Service (AKS) clusters and containerized ML workloads.
- Ensure model governance, versioning, and reproducibility using tools like MLflow and Azure DevOps.
- Experience with Azure Machine Learning, Azure OpenAI, Azure DevOps, and AKS.
- Proficiency in Python, Docker, Kubernetes, and CI/CD pipelines.
- Experience with LLM fine-tuning, prompt engineering, and model deployment.
- Familiarity with MLflow, Terraform, and monitoring tools like Prometheus/Grafana.
- Collaborate with data scientists and domain experts to understand project objectives and define modeling approaches.
Numbers & Facts
| Location | Princeton, NJ |
| Industry | Other/Not Classified |
| Company Size | 100 to 499 employees |
Skills
Analysis Skillsunmatched
Application Programming Interface (API)unmatched
Artificial Intelligence (AI)unmatched
Cloud Computingunmatched
Continuous Deployment/Deliveryunmatched
Continuous Integrationunmatched
Data Scienceunmatched
Data Setsunmatched
Deep Learningunmatched
DevOpsunmatched
Dockerunmatched
Documentation Modelsunmatched
Machine Learningunmatched
Microsoft Windows Azureunmatched
Performance Metricsunmatched
Performance Modelingunmatched
Performance Tuning/Optimizationunmatched
Production Systemsunmatched
Python Programming/Scripting Languageunmatched
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