Full Stack AI Engineer
Tasks
- Build data pipelines and integrate data sources
- Build interactive dashboards and AI driven user experiences
- Build scalable RESTful APIs and GraphQL APIs
- Collaborate with cross-functional teams
- Conduct code reviews and follow best practices
- Deploy applications and models to cloud environments
- Design and implement AI/ML solutions
- Develop AI service APIs
- Develop microservices architectures
- Develop responsive web applications
- Ensure high availability performance tuning and observability
- Ensure security compliance and enterprise governance
- Implement CI/CD pipelines
- Implement authentication authorization and secure API access
- Implement monitoring, logging, and alerting
- Integrate frontend with backend AI services
- Integrate ml models into production applications
- Manage ML model lifecycle training deployment monitoring retraining
- Optimize model performance latency scalability monitoring
- Participate in solution architecture and design discussions
- Translate business requirements into technical solutions
- Use Infrastructure as Code for deployments
Perks/Benefits
- N/A
Skills/Tech-stack
ARM | AWS | Agile | Alerting | Angular | Authentication | Authorization | Azure | Azure ML | Bicep | CI/CD | Datapipelines | DevSecOps | Django | Docker | FastAPI | Flask | GCP | GraphQL | GraphQL APIs | Infrastructure as Code | JavaScript | Kubernetes | Logging | MLOps | Machine Learning | Model Monitoring | NoSQL Databases | OAuth | Observability | PyTorch | Python | REST | REST API | React | Relational databases | SageMaker | Scikit-learn | Scrum | Secure Coding | TensorFlow | Terraform | TypeScript | Vertex AI | Vue | “as-code”
Education
N/A
Roles
AI | AI Engineer | Developer | Engineer | Full Stack | Full Stack Developer | Learning Engineer | Machine Learning Engineer | Stack Developer
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