Companies in this catalog do not charge applicants. Never send money to apply.
About the Role
We're looking for an AI Engineer to design, build, and scale the systems that power our AI-driven product. You'll work at the intersection of large language models, multi-agent systems, and production software, turning cutting-edge AI capabilities into reliable, user-facing experiences.
What You'll Do
Design and implement LLM-powered pipelines, including prompt engineering, context management, and response synthesis
Build and optimize multi-agent orchestration systems where AI components interact, reason, and produce coherent outputs
Develop and maintain integrations with foundation model APIs (Anthropic, OpenAI, and others), managing latency, cost, and reliability at scale
Implement retrieval-augmented generation (RAG) and memory systems for persistent, context-aware behavior
Partner with the Evaluation/QA Engineer to build frameworks that measure output quality, coherence, and factual grounding
Collaborate with product and design to translate workflows into robust technical experiences
Ship production features across the stack, from the model layer to the application
Monitor, debug, and improve system performance, token efficiency, and guardrails
3+ years of software engineering experience, with hands-on work building LLM-powered applications
Strong proficiency in Python and/or TypeScript/JavaScript
Experience with LLM APIs, prompt engineering, and agentic frameworks (e.g., LangChain, LlamaIndex, or custom orchestration)
Familiarity with vector databases and RAG architectures (Pinecone, Weaviate, pgvector, etc.)
Understanding of multi-agent systems, tool use, and function calling
Experience designing evaluation and testing strategies for non-deterministic AI outputs
Solid grasp of API design, async processing, and scalable backend architecture
Comfort working in a fast-moving, ambiguous, early-stage environment
Experience with real-time streaming (WebSockets, SSE) for conversational interfaces
Background in fine-tuning, model distillation, or inference optimization
Familiarity with cloud infrastructure (AWS/GCP/Azure) and CI/CD
