AI Native Engineer

2 to 5 years of software engineering experience in production environments

Minimum 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment - non-negotiable

Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent - at production depth, not tutorial level

Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs

RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering

LLMOps fundamentals: eval harness design, prompt versioning, and production observability

Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm)

Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience

Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure