AI Platform Engineer Lead

AI Platform Engineer Lead

We're seeking a AI Platform Engineer to lead the development and management of cutting-edge AI infrastructure for our complex enterprise environment. In this role, you'll harness your expertise to build infrastructure which enables your team and the firm to manage autonomous agents, machine learning systems and other applications in the AI domain.

You'll design and manage federated AI ecosystems, including MCP servers, agents and enterprise AI tooling, while orchestrating agent deployment across distributed systems and service buses. This role demands technical depth, strong communication, and a passion for building transformative AI infrastructure.

If you're ready to shape the future of AI and thrive in a fast-paced, innovative environment, we want to hear from you

Key Responsibilities:

• Design and implement AI infrastructure to support federated agent development and distributed deployment across the organization.
• Manage and orchestrate AI agents to generate architecturally sound code, ensuring adherence to enterprise standards and best practices.
• Build and maintain MCP (Model Context Protocol) servers and related infrastructure for agent communication, coordination, and enterprise service bus integration.
• Accelerate code review processes by leveraging AI agents for automated analysis, quality checks, and architectural validation.
• Establish robust monitoring, governance, and security frameworks for AI agent operations in enterprise environments.
• Implement and manage secure authentication and authorization systems for MCP servers, including OAuth flows and API security.
• Collaborate with development teams to integrate AI-powered tools into existing workflows and CI/CD pipelines.
• Act as a deployed engineer, traveling to different teams across the firm to teach AI infrastructure best practices and guide adoption.
• Provide technical leadership in transitioning traditional development practices to AI-augmented approaches.
• Mentor team members on AI infrastructure best practices and emerging agent management technologies.

Qualifications:

• Experience: 8+ years of professional software development experience with a demonstrated ability to architect and build scalable systems.
• AI Infrastructure: Experience with AI/ML infrastructure, agent orchestration, or similar AI-powered development tools.
• Programming Skills: Proficiency in Python, TypeScript/JavaScript, and experience with AI/ML frameworks and libraries.
• Enterprise Architecture: Strong understanding of enterprise software architecture patterns and distributed systems design.
• Cloud Platforms: Experience with cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code tools.
• Containerization: Experience with Docker, Kubernetes, and container orchestration in production environments.
• API Design: Experience building and managing APIs, microservices, and event-driven architectures.
• Security: Strong understanding of information security principles, OAuth 2.0/OpenID Connect flows, API security, and secure system design.
• Communication: Strong communication and teaching skills with the ability to work as a deployed engineer across different teams.
• Education: Bachelor's or Master's degree in Computer Science, or related technical or engineering field.

Preferred Qualifications:

• Experience with AI agent frameworks, LLM integration, or Model Context Protocol (MCP) implementations.
• Background in building federated systems or multi-tenant AI platforms.
• Experience with AI governance, security, and compliance in enterprise environments.
• Background in information security, penetration testing, or security architecture roles.
• Familiarity with code generation tools, automated testing frameworks, and AI-powered development workflows.
• Knowledge of vector databases, embedding systems, and retrieval-augmented generation (RAG) architectures.
• Experience in DevOps/Platform Engineering roles with focus on developer tooling and productivity.
• Previous experience managing transitions from traditional to AI-augmented development practices.