AI Platform Engineer

Responsibilities

• As an AI Platform Engineer, you will be a key architect of the software and systems that empower our AI-driven initiatives.

• More than just managing infrastructure, you will be actively building the cohesive, end-to-end platform that enables our teams to develop, deploy, and scale applications rapidly.

• You will engineer robust backend services, create internal tools, and design the automated workflows that form the foundation of our AI capabilities.

• Develop Core Platform Services: Design, build, and operate the backend services and APIs that serve our AI models and manage the ML lifecycle.

• Develop the Data Ingestion Platform: Design, build, and maintain the services and automated workflows responsible for populating our vector and graph databases, ensuring our AI applications have access to timely and accurate data.

• Build Full-Stack Tooling: Create user-facing tools and internal dashboards that allow developers, data scientists and managers to interact with the AI platform.

• Own the Application & Model Lifecycle: Implement and maintain the core platform components and ensure the performance of production systems.

• Collaborate and Enable: Work closely with software and data science teams to understand their needs, gather requirements for the platform, and provide the tools and support that accelerate their work.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

• Proven experience as a Software Engineer, Platform Engineer, or an AI/MLOps Engineer with a strong software development background.

• Strong proficiency in Python or Go, with a track record of building scalable, production-grade applications.

• Experience designing and building APIs and backend services, ideally with frameworks like FastAPI.

• A solid understanding of AI/ML concepts, particularly Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and vector databases.

• Hands‑on experience with Docker and Kubernetes for containerization and orchestration.

• Experience with Infrastructure as Code (e.g., Terraform) and cloud platforms (preferably AWS).

• Familiarity with modern JavaScript/TypeScript and a UI framework (e.g., React, Vue) for building internal tools is a significant plus.