AI/ML Engineer
Tasks
- Build automated model training and monitoring pipelines
- Collaborate to prepare data and operationalize models
- Create model serving for batch and real time inference
- Design production AI ML services
- Implement MLOps with CI CD and source control
- Implement model explainability and governance
- Implement security access control auditing and governance
- Manage model versions artifacts datasets and features
- Monitor model performance and drift
- Optimize AI ML services for performance scalability reliability and cost
Perks/Benefits
- 401k match
- Access to sports tickets
- Dental insurance
- Federal Holidays
- Flexible PTO
- Flexible spending account
- Health insurance
- Life insurance
- Long-term disability
- Metro Reimbursement
- Paid Cloud Developer Accounts
- Parking reimbursement
- Professional development
- Referral bonuses
- Short-term disability
- Social events
- Training certifications conferences
- Vision insurance
Skills/Tech-stack
API Development | AWS | Automated testing | Azure | CI/CD | Cloud deployment | Container Orchestration | Data Pipelines | Data Processing | Data Versioning | Distributed data | Distributed data processing | Docker | Drift Detection | Feature Engineering | Google Cloud | Infrastructure as Code | Kubernetes | MLOps | Machine Learning | Microservices | Model Evaluation | Model Explainability | Model Governance | Model Monitoring | Python | Source Control | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI/ML Engineer | Engineer | ML Engineer
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