Overview
We are seeking an AI Infrastructure Engineer to design, build, and scale the foundational infrastructure that enables AI-driven development across the organization. This role will focus on building secure, production-grade systems that support intelligent agents, large language models (LLMs), and distributed AI tooling. The ideal candidate combines strong software engineering fundamentals with hands-on experience in AI infrastructure and Site Reliability Engineering (SRE) experience.
Responsibilities and Impact
- Build Intelligent Agent Platforms: Design and implement orchestration frameworks and secure execution environments that enable LLM-powered tools and agent-based workflows.
- Architect AI Infrastructure: Develop scalable infrastructure that supports distributed agent development and deployment across multiple teams and business units.
- Oversee AI Agent Operations: Manage agent lifecycles and ensure AI-generated outputs — including code — align with architectural standards, security policies, and engineering best practices.
- Develop Agent Communication Systems: Build and maintain Model Context Protocol (MCP) services and supporting infrastructure to enable reliable communication, coordination, and integration with enterprise systems.
- Implement Governance & Security Controls: Establish monitoring, observability, compliance, and security frameworks to ensure safe and responsible AI operations at scale.
- Drive Organizational Adoption: Partner directly with teams across the firm to promote AI infrastructure best practices, provide hands-on guidance, and support adoption of AI-augmented development workflows.
Our future colleague has these skills
- 5+ years of professional software engineering experience, with a proven track record of designing and delivering scalable, production-grade systems.
- Demonstrated experience managing Kubernetes in a production environment.
- Expertise in building and supporting AI/ML infrastructure, agent-based systems, or AI-enhanced developer platforms.
- Solid understanding of modern security architecture, including API security, OAuth 2.0 and Keycloak authentication flows, and secure system design principles.
Bonus Skills
- Hands-on experience with AI agent frameworks, LLM integrations, or Model Context Protocol (MCP) implementations.
- Experience designing multi-tenant or federated AI platforms operating across distributed environments.
- Familiarity with enterprise AI governance, compliance frameworks, and operational risk controls.
- Background in cybersecurity, security architecture, or penetration testing.
- Experience working with AI-assisted development tools, automated code generation, and modern testing frameworks.