AI Engineer
Tasks
- Apply security and access controls
- Automate training evaluation approval deployment and rollback
- Build AI pipelines
- Create runbooks and SLAs
- Define evaluation and monitoring for GenAI outputs
- Deploy models as APIs
- Deploy models as batch jobs
- Design AI services
- Ensure auditability and regulatory traceability
- Ensure reliability scalability observability cost and security
- Ground outputs in approved data sources
- Handle data classification and privacy controls
- Implement AI governance by design
- Implement LLM delivery pipelines
- Implement ML delivery pipelines
- Implement agent workflows
- Implement controlled releases
- Implement embeddings
- Implement environment isolation
- Implement model versioning and reproducibility
- Implement monitoring and alerting
- Implement retrieval-augmented generation
- Implement semantic search
- Manage production incidents for AI systems
- Support model risk management
Perks/Benefits
- Benefits
- Community engagement opportunities
- Cross-functional development opportunities
- Flexible vacation
- Online courses
- Personal days
- Sick days
- Tuition assistance
- Workplace accommodations
Skills/Tech-stack
APIs | Agent workflows | Automation | Azure | Cloud infrastructure | Cloud platform | Embeddings | Generative AI | Google Cloud | Google Cloud Platform | LLMOps | MLOps | Machine Learning | Model Monitoring | Model versioning | Observability | Python | Reproducibility | Retrieval-Augmented Generation | SQL | Semantic Search
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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