About the Role
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.
Responsibilities
•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
•Building 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
•Building 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
•Running pipelines against real client data and real production volume, with results visible in everything that ships
Requirements
•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)
•Thinking in systems, not prompts — how components interact, fail, and scale
•Thinking in constraints — how to guide models toward predictable outputs
•Thinking in data — extraction, classification, and structure as the foundation everything else stands on
•Pragmatic approach — you ship working solutions and refine based on results
•Ability to translate abstract creative direction into structured AI workflows
•Ability to turn 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
Conditions
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.
