AI Infrastructure Engineer – Agents & ML Systems
Tasks
- Build internal AI infrastructure connecting LLMs and AI agents with internal tools and data
- Build observability logging tracing auditability and monitoring tools
- Build tool integration and connector infrastructure for AI agents
- Create retrieval augmented generation pipelines and context management
- Develop and maintain agentic AI systems for task automation and analysis
- Develop evaluation frameworks for agent performance and model quality
- Implement ML infrastructure workflows for data preparation and model evaluation
- Maintain documentation templates and best practices
- Secure agentic AI systems with least privilege sandboxing and misuse mitigation
Perks/Benefits
- 401k matching
- Equity package
- Global Entry
- Health and wellness stipend
- Health, dental, vision insurance
- Life insurance
- Paid parental leave
- Unlimited PTO
- Work from home stipend
Skills/Tech-stack
Airflow | Amazon S3 | Backend Development | C++ | Dagster | Data Pipelines | Debugging | Distributed Systems | Docker | Embeddings | Experiment tracking | Fine Tuning | Go | Human-in-the-loop | Kafka | Kubernetes | Least Privilege | Logging | MLOps | MLflow | Machine Learning | Model Deployment | Model Evaluation | Observability | PostgreSQL | Prompt engineering | Prompt injection | Prompt injection mitigation | Python | RAG | REST APIs | Ray | Retrieval-Augmented Generation | Secrets management | Secure tool execution | Security | The Loop | Tool Execution | TypeScript | Vector Search | Weights and Biases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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