AI Platform Engineer

Overview

We are looking for an experienced AI Platform Engineer with 6 to 12+ years of expertise to design, build, and manage a unified AI platform infrastructure. This infrastructure supports multiple AI teams by providing a robust foundation that eliminates the need for redundant deployment processes across different practices.

Key Responsibilities

• Develop and sustain shared AI platform infrastructure, including compute resource provisioning, network setup, and Identity and Access Management (IAM) tailored for AI workloads.
• Take ownership of internal tools and deployment templates that enable consistent model and agent deployment across teams.
• Implement standardized CI/CD pipelines for AI workloads across various practices, incorporating shared AI evaluation environments.
• Oversee platform-wide cost governance and capacity management amidst concurrent projects.
• Partner with AI Security Engineers to maintain and enhance the platform’s security posture.
• Collaborate with MLOps and LLMOps Engineers at the intersection of platform infrastructure and workload-specific operations.
• Manage and prioritize infrastructure requests from multiple practice leads effectively.
• Produce comprehensive documentation of platform capabilities to facilitate self-service by AI teams.
• Forecast platform expenditures and justify costs to leadership audiences lacking technical backgrounds.

Candidate Requirements

• Between 6 and 12+ years of experience in platform or infrastructure engineering, including a minimum of 2 years supporting AI or ML workloads.
• In-depth knowledge of cloud infrastructure technologies such as Infrastructure as Code (IaC), Kubernetes, networking, and IAM, with experience hosting vector and graph databases.
• Proven track record in building internal developer platforms and tooling rather than solely managing infrastructure operations.
• Experience integrating and operating managed AI platforms like Microsoft Azure AI Foundry, AWS Bedrock, and Google Vertex AI, alongside open-source self-hosted platforms, is advantageous.
• Understanding of multi-tenant capacity planning and cost allocation methodologies.
• Strong experience in platform-level security enhancements and hardening.
• Ability to engage and manage stakeholders across different AI practices, balancing competing infrastructure demands.
• Financial literacy to accurately forecast and explain platform spending to non-technical managers.
• Strong documentation skills enabling AI teams to adopt and self-manage platform capabilities effectively.
• Collaborative mindset that fosters the creation of shared infrastructure solutions without becoming a process bottleneck.
• Key success indicators include platform reliability and uptime, cost efficiency relative to budget, and the adoption rate of self-service platforms by practices.