Sr. Applied AI Engineer
Tasks
- Architect context engineering patterns
- Build Retrieval Augmented Generation systems
- Build and integrate REST APIs
- Build evaluation infrastructure and measure success criteria
- Create retrieval and generation pipelines
- Deploy and monitor machine learning and LLM services
- Design and ship production large language model systems
- Design and ship production machine learning systems
- Develop agentic workflows to act on internal data
- Implement OAuth authentication
- Implement data governance and access scoping
- Implement data warehouse SQL feature engineering
- Implement model regression testing and monitoring
- Integrate webhooks
- Maintain versioned traceable LLM workflows
- Manage retraining cadence and rollback paths
- Perform supervised model ranking and prediction
- Track feature drift and outcome metrics
Perks/Benefits
Skills/Tech-stack
CI/CD | Containerization | Context engineering | Data Governance | Databricks | Docker | Embeddings | Information Retrieval | Infrastructure as Code | Language Models | Large Language Models | Machine Learning | Model Evaluation | Monitoring | OAuth | Prompt engineering | Python | RAG | REST API | Retrieval-Augmented Generation | SQL | Supervised Learning | Unity Catalog | Webhooks | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Regions
Countries
States
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