AI engineer for brand content

Grip is changing how global brands produce content - from manual, tool-driven workflows to intelligent systems that generate high-quality visual output at scale. As a Senior AI Engineer, you build the intelligence behind that shift: LLM agents that understand what a brand looks like, retrieval systems that ground every generation in real brand knowledge, and automated pipelines that turn messy client material into structure machines can work with.

• designing focused, reliable agents that translate brand intent into precise creative direction
• crafting, testing, and versioning the prompts that steer agents and generation, and building the system that assembles them - prompting as an engineering discipline, not trial and error
• grounding generation in each brand's rules, assets, and visual language, with strict isolation between clients
• systematic measurement (RAGAS or similar), A/B testing, and regression gates, so quality is proven rather than assumed
• pipelines that pull structure out of documents, decks, scans, and imagery; OCR and vision models for reading, understanding, and tagging whatever a client hands us
• end-to-end pipelines that run without babysitting: classify, extract, propose, verify - humans review outcomes instead of doing the work by hand
• fine-tuning LLMs and neural networks where prompting hits its ceiling, running models locally or hosted depending on what the job needs
• your pipelines run against real client data and real production volume, and the results are visible in everything that ships

• You build systems that make AI predictable, controllable, and production-ready — especially in visual domains.
• Strong experience with LLM-based systems in production (agents, RAG, orchestration)
• Hands-on work with evaluation frameworks and structured experimentation — quality you can prove, not claim
• Solid programming skills across TypeScript and Python, with experience shipping working systems
• Experience training or fine-tuning models and managing their lifecycle
• Exposure to generative image pipelines and what it takes to make their output usable (not theoretical interest — actual contact)
• In systems, not prompts — how components interact, fail, and scale
• In constraints — how to guide models toward predictable outputs
• In data — extraction, classification, and structure as the foundation everything else stands on
• Pragmatically — you ship working solutions and refine based on results
• Translating abstract creative direction into structured AI workflows
• Turning messy, unstructured material into clean, reliable data pipelines
• Debugging LLM and retrieval behavior across multiple layers of a pipeline
• Communicating across engineering and creative teams

• Grip is infrastructure for enterprise creative production—powering global brands to generate content at scale with control and consistency.
• AI-generated content is moving from experimentation to production. The challenge is no longer generation—it’s control, reliability, and scale.
• You’ll work with ComfyUI-based workflows, multi-tenant architecture, and a stack spanning TypeScript, Python, Elixir, and C#—built for production, not demos.
• You will define how AI is actually used in real-world creative systems—turning unpredictable models into dependable pipelines.