Build and operate Python-based AI agent systems, translating model capabilities into reliable product workflows, integrations, and reusable platform abstractions while improving quality, latency, cost, and safety.
Senior AI Engineer
📜 Description
- Build LLM agents that translate brand intent into precise creative direction.
- Craft, test, and version prompts to steer agents and generation, treating prompting as an engineering discipline.
- Ground generation in each brand's rules and assets, ensuring strict isolation between clients.
- Conduct systematic measurement and A/B testing to ensure quality is proven.
- Develop pipelines for data extraction and OCR to structure client materials.
- Create end-to-end automation pipelines that require minimal human intervention.
🛠️ Requirements
- 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
Full job description
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
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