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.
The hard problem in generative AI is no longer generation. It's control, consistency, and trust — making unpredictable models behave reliably for brands that care deeply about how they show up in the world. That's the problem you'll own.
What You'll Work On
LLM agents - designing focused, reliable agents that translate brand intent into precise creative direction
Prompt engineering & orchestration - 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
Retrieval & RAG - grounding generation in each brand's rules, assets, and visual language, with strict isolation between clients
Evaluation & experimentation - systematic measurement (RAGAS or similar), A/B testing, and regression gates, so quality is proven rather than assumed
Data extraction & OCR - 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
Automation - end-to-end pipelines that run without babysitting: classify, extract, propose, verify - humans review outcomes instead of doing the work by hand
Model training - fine-tuning LLMs and neural networks where prompting hits its ceiling, running models locally or hosted depending on what the job needs
Client-facing impact - your pipelines run against real client data and real production volume, and the results are visible in everything that ships
Who You Are
You build systems that make AI predictable, controllable, and production-ready — especially in visual domains.
You Have:
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)
You Think:
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
Your Skills Are:
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
Why Join Grip?
The Platform Position.
Grip is infrastructure for enterprise creative production—powering global brands to generate content at scale with control and consistency.
The Market Timing.
AI-generated content is moving from experimentation to production. The challenge is no longer generation—it’s control, reliability, and scale.
The Technical Moat.
You’ll work with ComfyUI-based workflows, multi-tenant architecture, and a stack spanning TypeScript, Python, Elixir, and C#—built for production, not demos.
The Work.
You will define how AI is actually used in real-world creative systems—turning unpredictable models into dependable pipelines.
The procedure
If you’re ready to bring your powers to a role where you can make a difference, we want to hear from you. Send your resume and portfolio in English via the link provided. If you have questions, contact our Recruitment Department at [email protected].
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