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Architectural Depth: You have a proven track record (typically 6+ years) of shipping production-grade backends for large-scale systems. You don’t just write code; you design for high throughput, low latency, and long-term maintainability
Data Engineering Savvy: You’re comfortable building high-throughput indexing pipelines that handle both the messy world of unstructured data and the rigid world of structured schemas
Retrieval Intuition: You understand that "search" is more than just a keyword match. You have direct experience (or deep theoretical knowledge) in semantic search, vector databases, hybrid retrieval strategies, or with traditional search engines like Elastic or OpenSearch
RAG & Orchestration: You understand the nuances of Retrieval-Augmented Generation (RAG) patterns, from embedding pipelines and hybrid search techniques to how query planning and metadata filtering can make or break an LLM's performance
Language Fluency: You are an expert in at least one major language like Go, Rust, C++, Java, or Python
Infrastructure: Familiarity and experience with modern infrastructure tools, such as Kubernetes, cloud-native architectures, and observability frameworks, as well as infrastructure-as-code tools like Terraform or Pulumi
Product Thinking: You don't just build to spec; you build for the user. You can design clean, intuitive APIs that both human developers and autonomous agents will love
Ambiguity Navigator: You’re comfortable in a high-growth environment. You prefer "owning a problem" over "executing a ticket."