Applied AI Engineer
Tasks
- Architect scalable APIs and backend systems
- Build RAG and agentic workflows
- Build data and inference pipelines
- Conduct code review testing and documentation
- Design production LLM services
- Design prompts and tool function calling
- Ensure reliability performance cost and observability
- Implement logging, metrics, tracing, and alerting
- Ingest process transform and serve datasets
- Integrate LLMs into production applications
- Run evals and continuous improvement in production
- Set up CI CD secure by default design
- Tune cost and latency for production
Perks/Benefits
- 401k retirement plan
- Dental benefits
- Life insurance
- Long-term disability
- Medical benefits
- Paid caregiver leave
- Paid parental leave
- Paid time off
- Short-term disability
- Vision benefits
Skills/Tech-stack
API Versioning | AWS | Agentic Workflows | Alerting | Arize | Authentication | Azure | CI/CD | Code review | Cost Optimization | Distributed Systems | Docker | Evaluation | Event Driven | Event-driven architecture | FastAPI | Flask | Function Calling | Git | Google Cloud | Infrastructure as Code | Kafka | Kubernetes | Langchain | Langsmith | Language Models | Large Language Models | Latency optimization | Llamaindex | Logging | Metrics | Observability | OpenTelemetry | Prompt engineering | Pulumi | Pydantic | Pydantic AI | Python | RAG | RESTful API | Streaming Platforms | Terraform | Testing | Tool/function calling | Tracing | Trunk-based | Trunk-based development | Version control | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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