Senior Applied AI Engineer
Tasks
- Build data pipelines and integrations
- Build session memory and context graph
- Collaborate with product design and people science
- Conduct bias testing and evaluation
- Continuously improve LLM systems
- Create context view from workplace data
- Create production guardrails and safety
- Design agentic AI features
- Design graph based agent frameworks
- Evaluate agentic AI capabilities
- Implement agentic AI systems
- Implement retrieval-augmented generation
- Maintain AI observability and monitoring
- Monitor and act on production evaluation results
- Own end to end feedback loop
- Perform prompt engineering and evaluation at scale
- Provide LLM powered performance diagnostics
- Translate requirements into technical solutions
- Write technical documentation
Perks/Benefits
- Coaching
- Equity
- Extended end of year break
- Home workspace setup budget
- Inclusive parental leave
- Learning programs
- Medical insurance
- Monthly Wellbeing Allowance
- Quarterly Refresh Days
- Social impact days
Skills/Tech-stack
Agent Orchestration | Agent systems | Bias Testing | Context engineering | Evaluation datasets | GraphRAG | Guardrails | Human-in-the-loop | Knowledge graph | LLM | LLM-as-judge | Langfuse | Langgraph | Langsmith | Multi-Agent | Multi-Agent Systems | Neo4j | Neptune | Observability | PGVector | Prompt engineering | Python | RAG | React | Retrieval-Augmented Generation | The Loop | Tracing | Vector Search
Education
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