Senior AI Engineer
Tasks
- Build automated evaluation pipelines
- Build tool-using agents
- Create evaluation frameworks
- Define compliance and privacy guidelines
- Design Agentic AI systems
- Develop multi-agent workflows
- Document system behavior and failure modes
- Engineer dynamic context
- Ensure AI service reliability and performance
- Implement observability and analytics
- Implement retrieval-augmented generation
- Implement safety guardrails
- Integrate AI features into backend services
- Monitor online metrics
- Prototype AI workflows
- Run experiments AB testing
- Translate business problems into AI workflows
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | APIs | AWS | Agent systems | Airflow | Anthropic | Automated Evaluation | Azure OpenAI | B testing | Backend Services | CI/CD | Celery | Cloud Platforms | Cloud platform | Context engineering | Cost Optimization | Datadog | Embeddings | Evaluation datasets | Event Driven | Event-driven architecture | Fallbacks | Function Calling | Google Cloud | Google Cloud Platform | Google Gemini | Grounding | Hallucination reduction | Honeycomb | Human-in-the-loop | Injection Defense | LLM APIs | LLM-as-judge | Langfuse | Language Models | Large Language Models | Latency optimization | Microservices | Multi-Agent | Multi-Agent Systems | Observability | Online monitoring | OpenAI | OpenTelemetry | PII handling | Prompt injection | Prompt injection defense | Prompt versioning | Python | RAG | Regression testing | Retries | Retrieval-Augmented Generation | Safety Guardrails | Sandboxed Tool Execution | Schema Validation | Temporal | The Loop | Tool Execution | Tracing | Vector Databases | Vertex AI | Workflow Orchestration
Education
N/A
Roles
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