AI/ML Engineer
Tasks
- Build RAG knowledge systems
- Build autonomous AI agents
- Build lightweight full stack interfaces
- Create ingestion and embeddings pipelines
- Deploy production AI workloads
- Design multi-agent workflows
- Develop backend APIs and services
- Evaluate emerging models and AI tools
- Implement model evaluation frameworks
- Implement security observability monitoring
- Implement vector search and reranking
- Measure latency reliability safety cost
Perks/Benefits
- 401k
- Employee assistance program
- Health spending account
- Life insurance
- Long-term disability
- Medical/Dental/Vision
- Paid time off
- Short-term disability
- Transportation benefits
Skills/Tech-stack
API Design | Asynchronous programming | Claude Code | Cloud platform | Cursor | Docker | Driven systems | Embeddings | Event Driven | Event Driven Systems | FastAPI | Gemini | GitHub Copilot | Google Cloud | Google Cloud Platform | Guardrails | Langchain | Langgraph | Langsmith | Microservices | Model Context Protocol | Model Evaluation | Monitoring | Next.js | Observability | OpenTelemetry | PGVector | Phoenix | Pinecone | Prompt engineering | Python | Qdrant | RAG | REST | React | Reranking | Security | Streaming APIs | Structured outputs | Tracing | TypeScript | Vector Databases | Vector Search | Vertex AI | Weaviate
Education
N/A
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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