Deployed Engineer (Charlotte)

Contenido del rol extraído en secciones para revisar más rápido.

Co-architect and co-build production AI agents with customer engineering teams
Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations
Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows
Advise customers post-sale on architecture, best practices, and roadmap-level decisions
Run technical demos, trainings, and workshops for developer audiences
Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers
Occasionally contribute code upstream when it meaningfully improves customer outcomes

3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up
Strong Python, JavaScript and systems fundamentals
Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling
Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations
Can explain technical tradeoffs clearly and build trust with developer audiences
Take responsibility for outcomes, not just recommendations
Have a bias toward action and enjoy figuring things out as you go
Are excited about operating AI agents in production, not just building demos

You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks
Worked with LLM evaluation, observability, or guardrails
Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts
Have shipped and operated production software and are comfortable owning systems under real-world constraints

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