AI Platform Engineer/SRE

Direct Hire!

4 days onsite in Lower Manhattan, one day remote.

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.

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

1. 5+ years of software engineering experience building scalable, production-level systems.
2. Strong hands-on experience managing Kubernetes and containerized infrastructure in live environments.
3. Experience supporting AI/ML platforms, developer enablement tooling, or distributed automation systems.
4. Solid understanding of secure application architecture, API security, authentication/authorization protocols, and infrastructure security principles.

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