Lead Cloud AI Platforms Engineer

At Signature Aviation, we are modernizing operations and customer experiences through advanced data platforms and artificial intelligence. We are seeking a Lead Cloud Engineer, AI Platforms to design, build, and operate the secure and scalable cloud infrastructure that powers next-generation AI and agentic systems across the enterprise.

In this role, you will lead the infrastructure architecture for AI platforms supporting multi-agent systems, large language models (LLMs), predictive analytics, and operational AI solutions. You will collaborate closely with AI engineers, data scientists, DevOps teams, and digital engineering teams to deliver reliable, scalable platforms that enable AI-driven capabilities across operations, commercial systems, and field environments.

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

• 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

• Develop Infrastructure-as-Code environments using tools such as Terraform to enable scalable and repeatable deployments

• 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