AI Agent Architect

About This Role

We are toogeza, a Ukrainian recruiting company that is focused on hiring talents and building teams for tech startups worldwide. People make a difference in the big game, we may help to find the right ones. Currently, we are looking for a AI Agent Architect for Elva. Location: Remote Job Type: Full-Time Overview: We are building an AI-driven video production system designed as an intelligent multi-agent orchestration layer capable of transforming raw ideas and references into fully structured video content. The system operates as a black-box creative engine for different types of users: professional video creators casual users creators of short narrative concepts Users provide an idea, references, or media fragments, and the system automatically orchestrates multiple AI agents responsible for analysis, scripting, editing, and production of video content. We are looking for an engineer who can design and implement multi-agent pipelines, orchestrate AI tools, and build intelligent workflows that combine video analysis, storytelling logic, and automated editing. This role sits at the intersection of AI systems architecture, creative tooling, and multimodal content generation. Responsibilities: AI Agent Architecture Design and implement the architecture of a multi-agent video editing system including agents responsible for: video analysis narrative generation editing orchestration production and output synthesis Define system prompts, behavioral rules, and structured instructions for agents interacting within the pipeline. Pipeline Orchestration (n8n) Develop and maintain complex orchestration pipelines in n8n, including: multi-agent workflows tool-calling logic dynamic routing between tools and models context passing between agents Pipelines must be capable of selecting the most appropriate models, tools, and strategies depending on the task. Multimodal Data Processing Design robust pipelines for handling: video materials image assets user text prompts structured metadata Ensure proper data transformation and context transfer across the pipeline stages. Tool & API Integration Integrate both external and internal APIs for multimodal generation and processing, including: image generation video generation speech synthesis audio generation video processing services Rapidly evaluate available APIs and select the best quality tools and models for each task. Model Orchestration & Optimization Tune and optimize model interactions, primarily based on Gemini models, including: prompt engineering structured outputs tool-calling workflows agent collaboration logic Optimize pipelines for quality, reliability, and execution efficiency. Future Architecture (RAG & Knowledge Systems) Design systems that support: vector databases retrieval-augmented generation (RAG) memory and contextual reasoning between agents Expected Outcomes: The pipeline system should be capable of: Taking an idea + references as input Analyzing the content Generating a coherent narrative structure Selecting appropriate visual and audio elements Producing a high-quality, structured video output Requirements: AI Systems & Agent Architecture Strong experience building multi-agent systems, including: intent and sub-intent modeling agent orchestration agent communication and transport layers summarization pipelines context passing between agents Workflow Orchestration Hands-on experience with: n8n Agent tool-calling n8n MCP Experience designing complex automation pipelines is essential. Programming Strong practical coding skills with vibe coding mindset: Primary languages: Python JavaScript Bonus experience: ComfyUI custom nodes lightweight APIs (e.g., HuggingFace Spaces or inference endpoints) Multimodal Tooling Knowledge Ability to quickly navigate API documentation and integrate tools for: image generation video generation speech synthesis audio generation multimodal analysis You should know where and how to obtain the best generation quality for each modality. Creative Thinking Strong sense of visual rhythm and composition Creative intuition and storytelling awareness Good taste in video structure and montage Ability to evaluate AI-generated output not only by metrics, but also by creative quality and narrative coherence Video & Media Processing Preferred experience with: FFmpeg video processing pipelines image processing workflows Nice to Have: The following qualifications are not mandatory, but will significantly strengthen a candidate’s profile during the evaluation process: Agentic Frameworks Deep understanding of frameworks such as LangGraph and LangChain for building complex cyclic state graphs and stateful agent systems. Advanced RAG & Memory Management Experience designing long-term memory systems for agents, including mechanisms for storing and retrieving successful execution patterns or scenarios to improve future performance through experience-based retrieval. Self-Correction & Reflection Experience building agents with feedback loops such as self-reflection, self-critique, or strategy correction, enabling them to verify their own actions and adapt execution logic dynamically. Evaluation & Observability Experience with tools for monitoring, debugging, and evaluating agent chains and prompt behavior, such as LangSmith, Arize Phoenix, or Promptfoo, with the ability to identify logical failures, quality regressions, and orchestration bottlenecks. Ideal Candidate: The ideal candidate is someone who naturally operates between engineering and creative production, capable of building systems that automate complex media workflows while preserving narrative coherence and aesthetic quality. You enjoy designing systems where AI agents collaborate to produce meaningful creative outputs. What’s next? If this role sounds like a fit — we’d love to hear from you! Just send over your CV and anything else you’d like us to consider. We’ll review everything within five working days, and if your background matches what we’re looking for, we’ll get in touch to set up a call and get to know each other better.

Responsibilities

The role involves designing and implementing the architecture for a multi-agent video editing system, defining agent rules, and developing complex orchestration pipelines using n8n. Responsibilities also include integrating various multimodal APIs for generation and processing, and optimizing model interactions, primarily with Gemini models.

Requirements

Candidates must have strong experience building multi-agent systems, including intent modeling and context passing, alongside hands-on experience with n8n for workflow orchestration. Practical coding skills in Python and JavaScript are required, along with the ability to integrate tools for various media generation tasks and a strong creative sense for video structure.

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