Lead Cloud AI Platforms Engineer

Signature Aviation; Orlando (FL); On-site; USD 140,000 - 170,000; Full time

Job summary

Signature Aviation seeks a Lead Cloud Engineer, AI Platforms to design, build, and operate secure, scalable cloud infrastructure powering AI and agentic systems across the enterprise. You will lead infrastructure for LLM platforms, MLOps pipelines, and edge deployments in aviation and enterprise environments.

You will collaborate with AI engineers, data scientists, DevOps, and digital engineering teams to ensure reliable platforms, strong governance, and cost-efficient operations across field

Qualifications

• 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.
• Bachelor's degree in Computer Science, Engineering, or a related technical field.

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.
• 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.

Skills

Cloud engineering

Kubernetes

MLOps

Security architecture

Scalability engineering

Education

10+ years of experience in cloud infra roles

Bachelor's degree in Computer Science or Engineering

Tools

Terraform

LangChain

LlamaIndex

Semantic Kernel

Job description

Job Description

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.

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.

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
• 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

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 practicesBachelor'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