AI Platform Engineer

About The Job AI Platform Engineer The AI Platform Engineer is responsible for designing, building, and operationalizing the organization’s AI platform capabilities across cloud infrastructure, enterprise systems, and customer-facing digital platforms. This role serves as a technical leader within the IT organization and partners closely with Digital Development, Enterprise Systems, Operations, and business stakeholders to create scalable, secure, and reusable AI-enabled platform services that enhance both internal operations and external digital user experiences.

A core function of this role is establishing and evolving the organization’s AI Service Layer and supporting platform architecture including AI orchestration services, reusable APIs, customer-specific deployment models, and scalable cloud-native platform services—while ensuring scalability, maintainability, operational reliability, and security across environments.

This role bridges AI engineering, cloud architecture, SaaS platform engineering, DevOps collaboration, and enterprise data enablement to ensure AI capabilities can be consistently deployed, integrated, governed, and scaled across both enterprise operations and customer-facing digital platform experiences. This is a REMOTE position.

Responsibilities AI Platform Architecture & Engineering: Design, develop, and maintain reusable AI platform services and infrastructure components that support enterprise AI initiatives and customer-facing digital platforms, including AI APIs, orchestration services, retrieval-augmented generation (RAG) pipelines, prompt management, vector search capabilities, model integration frameworks, and reusable backend AI services.

Digital Platform AI Enablement: Design and implement AI capabilities that enhance customer-facing digital platforms and SaaS applications, including intelligent workflows, conversational interfaces, recommendation systems, predictive insights, automation services, and AI-assisted user experiences. Partner closely with frontend, backend, and UX teams to ensure AI capabilities are seamlessly integrated into unified digital platform experiences.

AI Service Layer Development: Build and maintain a centralized AI Service Layer that standardizes how enterprise systems, digital platforms, and SaaS applications consume AI capabilities, ensuring reusable patterns for model access, prompt orchestration, logging, security, governance, and scalable AI integration across customer-facing and internal solutions.

Enterprise Data & AI Enablement: Partner closely with Data Integrations Engineering and Enterprise Systems teams to ensure enterprise data assets are accessible, structured, secure, normalized, and optimized for AI and analytics workloads across both internal and customer-facing systems.

SaaS Platform Architecture & Deployment Support: Support scalable multi-tenant and customer-specific SaaS deployment models, including customer environment provisioning standards, AI service deployment architecture, database isolation strategies, deployment automation, and platform scalability best practices.

Technical Leadership & Cross-Functional Collaboration: Collaborate closely with DevOps, Backend Engineering, Data Integrations, Enterprise Systems, Frontend Development, and UI/UX teams to define scalable AI integration standards, reusable AI-enabled development patterns, and AI platform best practices across the organization. Partner with business departments including Operations, Sales, R&D, and other functional teams to identify opportunities for AI-enabled process improvement, intelligent automation, predictive insights, and enhanced digital platform capabilities that align with organizational objectives and operational needs.

Qualifications The successful candidate will have significant experience designing and operationalizing scalable cloud-based AI and platform engineering solutions, with the ability to bridge AI systems, cloud infrastructure, SaaS platform architecture, enterprise data integration, and customer-facing digital experiences.

Specifically, The Candidate Should Have

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Engineering, or related field.
• 5+ years of experience in AI engineering, machine learning engineering, AI platform development, or related technical roles.
• Demonstrated experience b…