About the role

Staff Engineer at URBN developing AI-powered visual experiences. Integrating generative AI solutions with creative tools and workflows to build robust image and video generation systems.

Responsibilities

Design, build, and optimize image and video generation pipelines (generation, inpainting, upscaling, style transfer, conditioning, post-processing) into production-ready, observable services with attention to cost, latency, and throughput.
Design and develop agentic workflows (ADK, A2A, LangGraph, or similar), MCP servers (FastMCP or similar), and microservices (FastAPI, GraphQL, or similar) to orchestrate and serve generative AI capabilities at scale.
Develop prompt management systems and structured prompting strategies for consistent visual output, learning on the job how to wrangle prompts for fashion-specific and virtual try-on use cases.
Engineer consistency mechanisms and quality gates in partnership with data scientists who own evaluation methodology.
Integrate multimodal and vision-language models into production workflows for image understanding, automated tagging, captioning, and quality pre-screening.
Collaborate with Product Designers, Product Managers, Data Scientists, and other Engineers to translate brand and business needs into scalable generative AI solutions.
Evaluate new technologies, models, and vendors through proof-of-concept studies.
Implement and maintain ML architecture, including pipelines and applications that enable training and inference of generative models in production.
Champion best practices in full-stack algorithm engineering.

1+ year of hands-on experience building or operationalizing image generation systems (diffusion models, multimodal pipelines) in a professional applied context.
Working familiarity with the rapidly evolving landscape of image and video generation models and orchestration patterns.
Proficient with AI-powered development tools (e.g., Cursor, VS Code Copilot, Claude Code, or Gemini).
Experience designing agentic workflows or AI orchestration systems using LangGraph, ADK, A2A, CrewAI, or similar.
Familiarity with MCP (Model Context Protocol) is a strong plus.
Strong proficiency in Python, with practical experience using PyTorch and/or Hugging Face for model serving, fine-tuning support, and inference optimization.
Strong background in scalable RESTful APIs, microservices architecture, and high-availability distributed systems.
Proficiency with Docker, container orchestration, cloud-native services, and cloud-based AI infrastructure.
Experience with Terraform or similar IaC tooling.
Dedication to CI/CD pipelines, TDD, and automated code quality standards.
Excellent communicator who can bridge technical, data science, and creative teams.
Comfortable with ambiguity and minimal oversight.
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related quantitative field, or equivalent practical experience.