AI Engineer (Backend)

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content design, prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities:

• Build and iterate on prompt pipelines and multi-agent workflow components.
• Design and integrate agentic workflows that orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production.
• Deploy and serve open-source models, set up inference endpoints, manage GPU compute, and optimize for latency and cost.
• Write evals to compare outputs against references, quantify quality, and feed results back into the pipeline.
• Work on data pipelines: structured extraction from messy source text, localization, and similarity/deduplication.
• Debug and maintain pipeline stages in production.

Requirements:

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects.
• Solid Python fundamentals: clean, working, readable code.
• Hands-on experience with LLM APIs and prompt engineering (personal projects count).
• Comfort with Git, REST APIs, and working in a Linux environment.
• A feel for content and narrative; you can judge whether generated output is actually good, not just valid.
• Curiosity and clear communication: you ask good questions and don't stay stuck silently.

Preferred:

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI).
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector).
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar.
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU).
• Experience writing evals or LLM-as-judge scoring.
• Node.js and FastAPI familiarity, or experience deploying on AWS.