Title:  AI Platform Engineer

Header

We’re looking for an AI Platform Engineer with strong backend engineering skills and a passion for building systems that support AI-driven products. In this role, you’ll help design and maintain a robust, scalable foundation that connects backend services with AI systems, while also contributing to tools and workflows that empower our AI engineers. This position sits at the intersection of AI/ML engineering, data infrastructure, and platform development. You’ll collaborate closely with AI engineers, data scientists, and product teams to ensure backend services are reliable, performant, and aligned with product goals. At the same time, you’ll help shape internal tooling, development environments, and best practices to support technical workflows.

Job Summary

If you were to come on board as our AI Platform Engineer, we’d ask you to do the following:

• Build and maintain robust backend services and APIs to support AI features and data applications (e.g., using FastAPI, SQLAlchemy, Snowflake)
• Design infrastructure and data flows to support inference, experimentation, feature engineering, and system observability.
• Maintain production-grade reliability, security, scalability, and monitoring for AI systems and services.
• Collaborate with AI engineers and data scientists to productionize models and integrate AI and ML systems into user-facing applications.
• Support deployment, versioning, and maintenance of APIs, shared libraries, and tooling.
• Contribute to CI/CD pipelines, platform automation, and developer environments to improve team velocity.
• Partner with Product Managers, AI Engineers and Data Scientists to prioritize and deliver high-impact work.

Think you have what it takes to be our AI Platform Engineer? Here’s how we’ll know:

• Bachelor's or Master’s degree in a STEM field (or equivalent experience).
• 5+ years of experience in software engineering, with strong backend expertise.
• Proficiency in Python, SQL, and backend frameworks (e.g., FastAPI, Flask, or similar).
• Experience working with databases and data warehouses (e.g., Snowflake, PostgreSQL), and related ORMs or query frameworks (SQLAlchemy)
• Familiarity with ML pipelines, model serving, and data-intensive applications.
• Comfort working in cloud-native environments (e.g., AWS, GCP, Azure) and with containerization (Docker, Kubernetes).
• Exposure to MLOps tooling (e.g., MLflow, SageMaker, Vertex AI, BentoML)
• Strong communication and collaboration skills; able to work cross-functionally with technical and non-technical teams.
• Interest in AI observability, feature stores, or experiment tracking systems.