About this Job:
FIT:MATCH.ai is looking for a talented Full Stack Developer to enhance the architecture and functionality of our innovative SaaS platform. In this role, you will play a critical part in developing a scalable backend for our e-commerce platform, focusing on optimizing data processing and reducing latency for our expanding customer base Globally. This contract-to-hire position offers an opportunity to work remotely and transition to permanent status after 90 days.
What You'll Work On:
- Designing and developing a scalable backend for an e-commerce platform.
- Optimizing data processing and latency for a growing customer base.
- Enhancing the backend architecture and contributing to the system design.
Your Areas of Expertise:
- 5-7 years of Engineering work experience
- Proficiency in RESTful APIs and server-side languages (e.g., Python, TypeScript, JavaScript).
- Strong skills in Git, Linux, and AWS services (e.g., EC2, S3, Lambda).
- Experience with database management systems (e.g., Postgres, DynamoDB).
Bonus Skills:
- If you have exposure or experience in App Clip
- Experience in scaling widget implementations, optimizing performance
- Familiarity with PostgresQL and DynamoDB.
- Exposure to Docker and familiarity with Shopify.
- Experience in iOS software development.
- Experience managing multiple vendors.
What We Do
FIT:MATCH.ai is an award-winning B2B2C technology platform that enables apparel brands to match their customers with the best-fitting products in hard-to-fit categories.
Our patented algorithms start by deriving a shopper’s 3D body shape using augmented reality and Lidar. Next we match them with the closest digital twin of real body scans within our extensive database to make personalized product recommendations.
The first-of-its-kind SaaS platform is designed to eliminate brands’ and consumers’ questions about fit in order to drive conversion and loyalty, while reducing returns.
The FIT:MATCH digital twin matching solution:
- Drives a better omni-channel shopping experience
- Increases customer conversion and average order value
- Lowers customer returns
- Reduces waste in the design process
- Broadens the addressable market
- Includes a diverse shopper base
- Provides comprehensive data and insights
.png)
