Author: The Hub

Hello everyone,

PatternBreak, a creative consultancy/agency, is looking for skilled developers and engineers to collaborate on an innovative AI T-Shirt Generator project. This initiative is on behalf of a blank t-shirt brand and involves creating an advanced AI tool to generate photorealistic images for e-commerce use.

Project Overview:

• Objective: Develop an AI-powered generator capable of producing photorealistic images of t-shirts on AI-generated human models.
• Key Features:
  • Generate realistic images of t-shirts in various colors, textures, and styles.
  • Create consistent, photorealistic non-existent human models wearing the t-shirts, ensuring uniformity for model turnarounds in e-commerce.
  • Ability to prompt specific pre-cast AI fashion models (e.g., “James wearing Style 123 in blue, front view”).

Project Phases:

1. Proof of Concept (POC):

   • Develop a generator that can produce images of two unique AI fashion models (one male, one female) wearing one style of t-shirt in at least three color variations.
   • Models should be shown against a white seamless background with consistent lighting.
   • Ensure the AI-generated models are consistent for turnarounds, maintaining uniformity across different angles and poses.
   • Example prompt: “Jenna wearing Style 1420 in black, side view.”

2. Final/Ultimate Deliverable:

   • Expand the POC to include more styles, colors, and additional pre-cast models.
   • Detailed scope to be defined based on the outcomes of the POC.

Submission Requirements:

• Detailed Statement of Work (SOW) for the POC phase.
• Budget and pricing for the POC phase.
• Qualifications and relevant experience.
• References and case studies of similar projects.

Due to the confidential nature of this project, an NDA is required before receiving the full RFP.

Interested? Please email us at 

in**@pa**********.com











 for more details on how to respond to the RFP.

We look forward to collaborating with passionate and talented professionals!

Best regards,

Chevon Hicks

Head of Digital, PatternBreak

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About the Team

Workday is the cloud-based SaaS company providing industry-leading software for HR, financials, workforce planning and employee learning to more than 40% of the Fortune 500. We are the Machine Learning Product team at Workday and our focus is on the application of ML and statistical analysis to Workday’s products to serve our end users. We use diverse datasets to build data-driven products, which help the world’s largest organizations uncover insights and make strategic decisions about their people, finances, and business. Are you prepared to work on data with high velocity, volume and variety, and employ a modern machine learning distributed computing and big data software stack to address these challenges? We are expanding our MLOps/CloudOps team.

About the Role

In this role, you would:

• Partner and collaborate with multi-functional teams to deliver scalable, secure and reliable solutions
• Actively engage with data scientists, ML engineers, product managers and other architects across teams in requirements elaboration and drive technical solutions
• Own and develop features from end to end including infrastructure as code.
• Design and build solutions for efficient organization, storage and retrieval of data to enable meaningful scale
• Build systems and dashboards to supervise service & ML health.
• Lead in architecture reviews, code reviews and technology evaluation.
• Research, evaluate, prototype and drive adoption of new ML tools with reliability and scale in mind
• Develop and maintain monitoring and alerting systems for proactively identifying and addressing issues within the AI/ML infrastructure

About You

Basic Qualifications for Principal

• 6 or more years of validated proven experience in DevOps or programming experience preferably in Python, Java or Scala with a Bachelor’s and/or Master’s degree, preferably in CS, or equivalent experience
• Design, implement, and maintain robust DevOps pipelines for deploying, monitoring, and scaling machine learning development and data engineering
• Keep abreast of industry trends and new technologies, providing recommendations for continuous improvement of our DevOps and machine learning practices
• Collaborate with other Machine Learning teams to improve not just the product, but efficiencies in engineering processes
• Optimize public cloud-based infrastructure (AWS, Azure, or GCP) to support the computational requirements of machine learning workloads
• Implement and coordinate CI/CD workflows to automate testing, integration, and delivery of ML components
• Ensure the security and compliance of ML platforms, implementing standard processes for encryption, data protection and access controls
• Experience in managing relevant tools like Databricks and Sagemaker to perform efficient computation and management of large scale data lakes
• Experience in supporting your work in production

Other Qualifications

• Experience implementing and operating distributed systems
• Experience with data and/or ML systems with ability to think across layers of the stack
• Experience with Databricks, Sagemaker, & Apache Spark
• Experience in leading or mentoring other team members

Workday Pay Transparency Statement

The annualized base salary ranges for the primary location and any additional locations are listed below.  Workday pay ranges vary based on work location. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workday’s comprehensive benefits, please click here.

Primary Location: CAN.ON.TorontoPrimary CAN Base Pay Range: $151,200 – $226,800 CADAdditional US Location(s) Base Pay Range: $163,400 USD – $294,000 USD

Our Approach to Flexible Work
 

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