AI Platform Engineer

Overview

We're looking for an experienced AI Platform Engineer to help develop and scale the core infrastructure powering next-generation AI capabilities across the enterprise. This individual will play a key role in building secure, resilient systems that support large language models, intelligent automation, and AI-enabled engineering workflows. The ideal candidate brings a strong background in software engineering, cloud-native infrastructure, and platform reliability.

Key Responsibilities

• Design and build infrastructure that supports AI-powered applications, autonomous workflows, and developer productivity tools.
• Develop scalable platforms for deploying and managing distributed AI services across multiple teams and environments.
• Maintain operational stability and governance for AI systems, ensuring outputs adhere to security, architecture, and engineering standards.
• Build integration layers and communication frameworks that allow AI services to interact reliably with internal applications and enterprise tooling.
• Implement monitoring, observability, and security controls to support safe and compliant AI operations.
• Partner with engineering teams to drive adoption of AI-enabled development practices and provide technical guidance on platform usage and best practices.

Desired Qualifications

• 5+ years of software engineering experience building scalable, production-level systems.
• Strong hands-on experience managing Kubernetes and containerized infrastructure in live environments.
• Experience supporting AI/ML platforms, developer enablement tooling, or distributed automation systems.
• Solid understanding of secure application architecture, API security, authentication/authorization protocols, and infrastructure security principles.

Preferred Experience

• Familiarity with AI orchestration frameworks, LLM integrations, or agent-based platforms.
• Experience designing shared or multi-environment AI infrastructure platforms.
• Knowledge of governance, risk, and compliance considerations related to enterprise AI adoption.
• Background in cybersecurity, platform security, or application testing methodologies.
• Exposure to AI-assisted software development tools and automated engineering workflows.