AI engineer for higher education

LearnWise AI is an AI-first scale-up building virtual assistants, instructor efficiency tools, and student engagement solutions for universities and colleges. We're a small team moving fast: the best idea wins no matter who says it, and every hire owns real scope from day one.

• Design how agents call LMS and institutional APIs, with permissions, human approvals, sandboxed execution, and audit.
• Model routing, provider fallbacks, in-region inference, usage limits, and cost control.
• Take new learning, admin, and multichannel experiences from design to production.
• Detect silent failures, measure quality, and feed fixes back into prompts, workflows, and agent logic.
• Grow the framework that connects SIS, CRM, ticketing, and other institutional systems.
• Propose, align, then build; review others' designs and code.

• 6+ years as a software engineer building production backend services.
• Production experience in more than one backend language (e.g., Python, Go, TypeScript/Node, Java, Rust, PHP).
• Working Python knowledge and interest in learning async Python.
• LLM features shipped to production: tool-calling, structured outputs, streaming, RAG, embeddings, and their failure modes.
• Reliability under third-party dependency: retries, timeouts, fallbacks, rate limits, provider outages.
• You deploy what you build: containers, CI/CD, and a cloud platform (AWS preferred).
• Clear, direct written and spoken English; you raise blockers early.
• Daily use of AI coding tools, e.g., Codex or Claude, and you verify what they produce.
• Technical leadership: led development of a larger project or system across several engineers.
• Architecture design of services or AI platform components that others built on.
• Будет плюсом: AI Ops or agentic workflows: agents that call external APIs and take actions, with guardrails; LLM gateways or routers (LiteLLM or similar) and multi-provider setups (OpenAI, Anthropic, Bedrock, Azure OpenAI, Gemini); Retrieval at scale, including self-hosted embedding models; Observability and evals for AI systems (OpenTelemetry, Logfire or similar); Data residency and multi-region deployments (GDPR, FERPA)

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