Principal AI Engineer
Tasks
- Apply ML Ops practices
- Build full stack web applications
- Collaborate with data platform teams
- Create documentation standards
- Define AI data and application strategy
- Deploy AI enabled products
- Design cloud-native backend services
- Detect model drift
- Develop APIs and data models
- Drive cross-functional execution
- Enable observability
- Ensure secure system design
- Frame machine learning problems
- Govern data privacy and security
- Implement event-driven architecture
- Implement microservices
- Integrate models with applications
- Lead machine learning development
- Mentor engineers
- Monitor production models
- Perform data exploration
- Plan model retraining
- Serve as technical escalation point
- Set testing and deployment standards
- Translate business needs into technical solutions
Perks/Benefits
Skills/Tech-stack
API Development | AWS | Azure | Backend Development | BigQuery | Cloud Native | Cloud platform | Continuous Deployment | Data Modeling | Data Privacy | Databricks | Drift Detection | Event Driven | Event-driven architecture | Experimentation | Google Cloud | Google Cloud Platform | Infrastructure as Code | Language Processing | ML Ops | Machine Learning | Microservices | Model Drift | Model Drift Detection | Model Monitoring | Natural Language | Natural Language Processing | Observability | PyTorch | Python | Retraining | SQL | Scikit-learn | Secure Systems | Snowflake | Software testing | Statistical Analysis | TensorFlow | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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