Machine Learning Engineer, Applied AI (Hybrid)
Tasks
- Build proof of concept artifacts
- Champion experimentation and continuous learning
- Communicate milestones and findings
- Conduct exploratory data analysis
- Configure AI performance monitoring
- Customize data pipelines
- Design LLM-driven workflows
- Design evaluation for AI workflows
- Design experiments and prototypes
- Develop AI workflows
- Engineer prompts
- Evaluate LLM driven workflows
- Guide team members
- Implement retrieval-augmented generation pipelines
- Integrate AI workflows into production
- Integrate emerging ML tools and technologies
- Optimize AI workflows for performance reliability scalability
- Provide AI expert advice to product and engineering
- Recommend improvements to AI workflows
- Scope and plan AI workflow execution
- Tune models
Perks/Benefits
- Employee resource groups
- Flexible time off
- Hybrid work schedule
- Meal reimbursement
- Paid Holidays
- Relocation bonus
- Work from home stipend
Skills/Tech-stack
AWS | Alerts | Argo Workflows | Argo Workflows CI/CD | CI/CD | Context Management | Data Analysis | Data Engineering | Data Pipelines | Exploratory Data Analysis | Human-in-the-loop | Kubernetes | LLM Evaluation | LLM orchestration | Langfuse | Language Models | Large Language Models | Machine Learning | Performance Monitoring | Prompt engineering | Prompt management | Python | RAG | Reproducible Research | Retrieval-Augmented Generation | The Loop | Vector Databases
Education
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