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Applied AI Engineer
Skills
APISoftware As A ServiceContextDatabasesFundamentalsReasoning SkillsRegression Testing
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Proven experience shipping LLM-powered products to production
- Experience operating AI systems at scale and handling real-world failure modes
- Strong understanding of model selection prompting strategies context management and reliability
- Experience building agentic workflows and tool-calling systems
- Experience with frameworks such as LangGraph CrewAI AutoGen or custom agent architectures
- Strong understanding of retrieval systems embeddings vector databases hybrid search and re-ranking
- Familiarity with GraphRAG is a plus
- Experience building evaluation frameworks for AI systems
- Familiarity with regression testing LLM evaluation methodologies and performance monitoring
- Experience with tools such as LangSmith Langfuse Braintrust OpenTelemetry or similar platforms
- Strong Python development experience in production environments
- Experience with TypeScript modern web frameworks and API development
- Comfortable working with cloud infrastructure containers CI/CD pipelines and Kubernetes
- Experience deploying and operating services in Azure environments is preferred
Nice to have
- Hands-on
- Production-focused
- Customer-facing
- Collaborative
- End-to-end ownership
Day to day
- Design, build, deploy, and operate production AI systems end to end
- Own AI-powered product features across application development, agent orchestration, retrieval, deployment, observability, and customer implementation
- Work closely with product engineering and customers to deliver impactful AI capabilities and establish production AI best practices