Backend, MLOps Engineer

Company Background
PiñataFarms AI is a team that builds advanced AI products, allowing anyone to become creative by recontextualizing content to come together with others. Our products are used by millions. With a track record of major cultural and tech successes, we are using computer vision, mobile hardware, and consumer tech to create a generational phenomenon. Backed by top Silicon Valley VC's and influential strategic investors, we are at the forefront of innovation.

Role Description
We’re hiring a Backend & MLOps Engineer to own the Node.js backend and Python-based ML infrastructure that powers multiple consumer apps. You’ll operate across AWS Amplify/AppSync, Firebase, and on-prem GPUs, ensuring our services remain reliable, scalable, and cost-efficient. We are open to remote for this role and have teams in both the US and Europe.

What You’ll Be Doing:

• Develop, deploy, and maintain backend APIs on AWS Amplify & AppSync (GraphQL) or GCP (Firebase)
• Manage Docker-based Python CV/LLM inference pipelines, primarily on on-prem GPUs
• Prototype rapid fixes or features, then harden them into production-grade systems
• Collaborate with the product and the iOS team to deliver new endpoints and incremental features while meeting reliability and cost targets
• Streamline and automate routine tasks with scripts and modern AI tooling
• Maintain data pulls and pipelines – schedule and automate extracts from BigQuery, DynamoDB for analytics and reporting

What We’re Looking For:

• 5+ years in backend engineering, DevOps, or MLOps
• Strong proficiency in Python and solid Node.js/TypeScript skills
• Hands-on experience with core AWS services; comfortable with GCP/Firebase basics
• Demonstrated ability to learn quickly, prototype fast, and automate relentlessly – you default to scripting or AI-powered tools over manual work
• Solid SQL skills and familiarity with data-pipeline best practices
• Excellent written and verbal communication skills, plus a reliable on-call record

Bonus Points

• Operating GPU workloads on-prem and in AWS/GCP
• Prior use of LLM-based or AI-driven tooling to accelerate testing, data pulls, or infrastructure operations