LLM Systems / AI Agent Engineer
Tasks
- Analyze agent failure modes
- Apply prompt caching
- Build production AI agents
- Deploy AI agents
- Design orchestration systems
- Develop evaluation datasets
- Establish scalable engineering practices
- Generate structured outputs
- Implement mitigations
- Implement observability and tracing
- Implement tool calling workflows
- Improve AI agent reliability
- Instrument LLM workloads
- Manage streaming context
- Monitor production AI agents
- Optimize LLM operating costs
- Route models for efficiency
- Run evaluation pipelines
Perks/Benefits
- Continuous learning
- Fixed working hours
- Fully remote
- Knowledge sharing
- Laptop provided
- Medical insurance
- Mentorship
- No weekend work
- Professional communities
Skills/Tech-stack
AI Agents | AWS Bedrock | AWS Lambda | Agent Orchestration | Agent systems | Context engineering | Evaluation | Guardrails | Injection Protection | LLM FinOps | LLM-as-judge | Langchain | Langfuse | Langgraph | Language Models | Large Language Models | MCP | Model routing | Multi-Agent | Multi-Agent Systems | Node.js | Observability | OpenTelemetry | Output Validation | PostgreSQL | Prompt Caching | Prompt injection | Prompt-injection protection | Serverless AWS | Streaming | Structured Output | Tool-Calling | Tracing | TypeScript
Education
N/A
Roles
AI | AI/Agent Engineer | Agent Engineer | Engineer | LLM Systems Engineer | Systems Engineer
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