Senior AI Engineer

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

Build AI-Controlled Content Systems Architect and ship AI Controllers that encode visual logic (composition, layout, constraints) into reusable systems Translate creative direction into structured pipelines (e.g., turning logo safe zones into enforceable spatial constraints) Design templates that produce consistent outputs across thousands of variations Develop and Scale AI Workflows Build and optimize ComfyUI pipelines integrating multiple models, refiners, and control mechanisms Combine segmentation, depth maps, and latent constraints to guide generation toward production-grade outputs Iterate on workflows based on output quality, failure modes, and client requirements Own Model Behavior and Output Quality Work hands-on with diffusion models, encoders, and latent space manipulation Define how prompts, conditioning, and control signals interact across the pipeline Ensure outputs meet brand and visual standards—not just “good images,” but usable assets Extend Platform Capabilities Contribute to new node definitions and AI capabilities within Grip’s platform Specify technical requirements for new features across Python/PyTorch systems Collaborate with engineers and creative teams to push what’s possible in automated content generation

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

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] . INDG Grip handles and uses personal data of job applicants in line with its Recruitment Privacy Policy .