About LearnWise AI

We’re an AI-first startup helping universities and colleges support students and faculty with smart, knowledge-driven tools. Small team, high ownership, real user impact. LearnWise is a place where the best idea wins—no matter who says it—and where innovation and growth are in our DNA.

You’ll work closely with our Head of AI, CTO, and Head of Product.

Why this role exists

We’re building AI-powered product features that need to be reliable, useful, scalable, and shipped fast. We need an engineer who can take meaningful product areas from idea to production: understand the user problem, design the system, build the backend, integrate with LLMs and AI APIs, ship the feature, observe how it behaves, and iterate.

This role sits at the intersection of backend engineering, product engineering, and applied AI systems. You’ll build APIs, services, data flows, integrations, and infrastructure—but you’ll also work directly with LLM APIs, RAG pipelines, tool-calling, agentic workflows, experimentation frameworks, and AI-specific observability.

This is not primarily an AI/ML research role. You will not be training foundation models or doing academic ML. You will, however, be building production-grade systems around LLMs and AI agents, and you should be genuinely fluent in how modern AI systems are designed, called, evaluated, debugged, and shipped.

What you’ll do

• Own and ship AI-powered product features end-to-endTake substantial features from concept to production: clarify the problem, design the system, build it, ship it, observe it, and iterate.
• Work closely with AI, Product, Design, and Engineering to turn product goals into pragmatic technical solutions.
• Balance speed, quality, reliability, and user impact in real production systems.
• Build backend systems for AI productsImplement, maintain, and scale Python/FastAPI services powering our chat assistants, student-facing workflows, faculty tools, and internal AI systems.
• Design APIs, service boundaries, data models, integrations, and async workflows.
• Refactor, stabilize, and improve core systems as we grow from early startup to scale-up.
• Work deeply with LLMs, RAG, tools, and agentsIntegrate backend services with LLM APIs, vector stores, embeddings, rerankers, retrieval pipelines, and tool-calling systems.
• Build robust patterns for prompts, tools, structured outputs, streaming, retries, fallbacks, guardrails, and failure handling.
• Help design agentic workflows that safely and reliably interact with external systems and internal APIs.
• Improve reliability, observability, and evaluationStrengthen logging, tracing, metrics, alerts, evaluations, and debugging workflows for AI features and backend services.
• Debug tricky production issues and identify root causes across backend, infrastructure, and AI-system behavior.
• Build systems that are inspectable, measurable, and maintainable—not black boxes.