Sr. AI Engineer
Tasks
- Apply MLOps best practices including model versioning drift analysis and MLflow
- Build and optimize end-to-end data pipelines
- Deploy secure scalable responsible AI on cloud platforms
- Design develop and deploy AI ML and generative AI models for operational efficiency
- Develop and fine tune generative AI models for summarization and conversational AI
- Ensure LLM security including output moderation access control and auditability
- Implement LLM architectures including RAG and agentic frameworks
- Integrate data pipelines with Snowflake
- Maintain code repositories in GitLab and participate in agile sprints
Perks/Benefits
- 401k
- Dental insurance
- Expense reimbursement for internet costs
- Life insurance
- Medical insurance
- Paid time off
- Pet insurance
- Vision insurance
- Wellness programs
Skills/Tech-stack
Access Control | Agentic AI | Auditability | CI/CD | Cloud platform | Data Pipelines | Data Warehousing | Docker | Drift analysis | Explainability | Fairness | Generative AI | GitLab | Google Cloud | Google Cloud Platform | Google Gemini | LLM security | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Model Drift | Model Drift Analysis | Model versioning | Orchestration | Output moderation | Prompt engineering | PySpark | Python | Quantization | REST APIs | Retrieval-Augmented Generation | SDKs | SQL | Snowflake | T-SQL | Transparency | Vertex AI
Education
Roles
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