Job Details
- Required Skills
- JavascriptTypeScriptPostgresLLM
Requirements
- Strong proficiency in JavaScript and TypeScript, including modern ecosystem tooling (npm, ES modules, bundlers, browser constraints).
- Deep understanding of AI systems, including agent architectures, LLM tooling, and practical trade-offs in production use.
- Experience building developer tools, platforms, or internal systems used by engineering teams.
- Hands-on experience designing or implementing evaluation frameworks for AI systems and task quality measurement.
- Familiarity with Postgres and database design principles.
- Experience with vector databases, embeddings, and tools such as pgvector or similar technologies.
- Strong product intuition for identifying valuable AI use cases and avoiding fragile or low-impact solutions.
- Strong focus on testing, reliability, and real-world validation of systems.
- Excellent communication skills and ability to collaborate across engineering, product, and documentation teams.
- Experience working in fast-moving, experimental, or platform-oriented engineering environments is highly valued.
Responsibilities
- Design, build, and maintain AI tooling surfaces such as MCP, agent skills, and developer-facing AI interfaces.
- Improve and evolve AI-driven dashboard assistants to support debugging, workflows, and self-serve product usage.
- Translate customer AI usage patterns into improved product experiences, abstractions, and documentation structures.
- Develop and maintain evaluation frameworks, instrumentation systems, and feedback loops to measure AI reliability and performance.
- Design ways to expose product knowledge and documentation effectively to AI agents and developer tools.
- Build reusable patterns and infrastructure for AI integration across applications, including Edge Functions, embeddings, and agent workflows.
- Centralize shared AI logic and reduce duplication across services and engineering teams.
- Ensure high reliability through strong testing, benchmarking, stress testing, and validation of AI systems.
- Collaborate with cross-functional teams including docs, frontend, API, and CLI to deliver cohesive developer experiences.
- Stay up to date with the evolving AI ecosystem and apply best practices to improve developer-facing AI capabilities.
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