Senior Cloud ML Engineer - AI Platform Infrastructure (Contract-to-Hire)

Senior Cloud ML Engineer – AI Platform Infrastructure

Contract-to-Hire (W-2 with Benefits)

100% Remote (CST Work Hours)

Our Fortune 50 healthcare client is hiring a Senior Cloud ML Engineer to implement and support enterprise AI Shared Services. This role focuses on cloud AI infrastructure, not data science or model building. You will help build the AI Gateway, deploy cloud-based LLM and ML services, enforce platform guardrails, and operationalize standardized MLOps and LLMOps patterns. This is a hands-on engineering role working closely with the Lead Cloud MLE to deliver secure, scalable AI platform capabilities.

Responsibilities:

• Build and configure cloud AI infrastructure supporting enterprise access to LLM, GenAI, and ML services across AWS and Azure.
• Implement features of the AI Gateway including routing, access controls, model catalogs, and usage tracking.
• Apply platform guardrails including PII/PHI protection, prompt defense, safety filters, and rate-limiting.
• Configure observability patterns including logging, monitoring, and compliance tracking for AI workloads.
• Deploy cloud-native inference infrastructure using SageMaker pipelines, training jobs, and inference endpoints.
• Build Infrastructure-as-Code modules to automate provisioning and updates for shared AI services.
• Integrate AI infrastructure components into CI/CD pipelines for code-first deployments.
• Collaborate with Cloud Engineering, Data Engineering, Data Science, and Security to ensure consistent and secure AI service delivery.
• Maintain documentation, patterns, and developer enablement resources for platform onboarding.

Requirements:

• Bachelor’s or Master’s degree in Computer Science.
• 10 years of experience across both cloud platform engineering and ML engineering.
• Hands-on experience integrating AI cloud services such as AWS Bedrock or Azure AI Foundry.
• Experience deploying and operating ML and LLM workloads with SageMaker pipelines and inference endpoints.
• Experience managing cloud AI/ML services in AWS or Azure, including secure access patterns, service configuration, and enterprise guardrails for LLM and GenAI workloads.
• Experience building and maintaining infrastructure-as-code and automation used to provision, update, and monitor AI platform components.
• Experience contributing to centralized platform capabilities such as shared gateways, role-based access, usage tracking, or developer enablement.
• Experience with Databricks, Delta Lake, or Unity Catalog for governance is preferred.
• Strong Git-based workflows and CI/CD experience supporting infrastructure deployments.