Senior AI Platform Engineer (Applied AI / LLM Ops)

iFIT's vision is to create the world's most holistic health and fitness platform — integrating physical fitness, mental health, nutrition, and recovery into a seamless, personalized experience. To accelerate that vision, we're building an AI-driven operating model that powers smarter innovation, greater efficiency, and intelligent decision-making across the organization.

We're looking for a Senior AI Platform Engineer — the first dedicated AI platform engineering role within Infrastructure — to lead the deployment, integration, and operationalization of large language models (LLMs) across iFIT. This is a hands-on role focused on building scalable AI-powered tools, embedding LLM capabilities into business workflows, and laying the foundation for enterprise-wide AI adoption.

You'll serve as the bridge between emerging AI capabilities and real-world business impact — enabling teams across IT, Product, and Operations to leverage AI safely and effectively. This is a greenfield opportunity with high ownership and autonomy, backed by executive visibility and support, with direct impact on operational efficiency, cost optimization, and product innovation. Beyond AI platform work, you'll also support broader platform and systems administration responsibilities, helping maintain and improve the infrastructure and automation that underpins our internal services.

Role Commitments

 

• Deploy, integrate, and operationalize LLM platforms (e.g., Claude) across internal systems and business workflows
• Design and build AI-powered tools, automation, and reusable services that drive measurable impact across teams
• Develop prompt frameworks, guardrails, and evaluation methodologies to ensure reliable, responsible AI use
• Establish observability, cost controls, and governance practices that support scalable, secure AI operations

 

Essential Duties & Responsibilities

AI Platform Engineering & Integration

• Build and maintain integrations with LLM APIs (e.g., Anthropic Claude, OpenAI)
• Develop backend services and APIs that support AI-powered applications
• Design reusable AI components for use across multiple teams
• Enable rapid prototyping and scaling of AI use cases organization-wide

Automation & Internal Tooling

• Develop AI-driven automation to reduce manual processes and operational overhead
• Build internal tools that enhance productivity, decision-making, and workflow efficiency
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