Lead Cloud AI Platforms Engineer

Responsibilities

• Design and implement cloud infrastructure supporting LLM platforms, vector databases, and model inference pipelines
• Build and operate scalable environments supporting agentic AI systems, predictive models, and enterprise AI applications
• Support AI-driven operational use cases such as dynamic pricing, demand forecasting, and ramp capacity optimization
• Implement and maintain MLOps pipelines supporting model training, deployment, monitoring, and lifecycle management
• Optimize cloud performance, scalability, and reliability for AI and data workloads
• Implement monitoring, logging, and observability platforms to ensure operational visibility and system performance
• Collaborate with security and compliance teams to ensure data protection, platform security, and regulatory compliance
• Enforce cloud governance, cost optimization, and operational resilience best practices
• Design and support infrastructure for edge computing solutions that enable AI capabilities in field operations environments
• Partner with engineering, data science, and platform teams to ensure seamless integration between AI infrastructure and enterprise systems

Qualifications

• Minimum Education and/or Experience: 10+ years of experience in cloud engineering, platform engineering, or infrastructure architecture roles
• Strong experience designing and operating containerized environments using Kubernetes and distributed systems architectures
• Experience supporting infrastructure for AI, machine learning, or advanced data platforms
• Experience supporting AI model deployment or inference platforms used in operational environments
• Strong knowledge of cloud networking, security architecture, and reliability engineering practices
• Bachelor’s degree in Computer Science, Engineering, or a related technical field

Preferred Qualifications

• Experience supporting LLM platforms, vector databases, and modern AI application architectures
• Familiarity with frameworks and tools such as LangChain, LlamaIndex, or Semantic Kernel
• Experience integrating with foundation models such as OpenAI GPT, Claude, LLaMA, or Gemini
• Experience supporting predictive analytics and data science platforms in production environments
• Familiarity with authentication and security frameworks such as OAuth2 and RBAC
• Experience supporting multimodal AI systems, including vision, speech, or structured data processing
• Experience designing infrastructure for edge computing or field-based AI deployments
• Familiarity with aviation, logistics, mobility, or other operational technology environments
• Experience working in regulated industries such as aviation, logistics, finance, or hospitality