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.With more than 225 locations worldwide, Signature Aviation is the largest global network of private aviation terminals, delivering safe, convenient, and elevated experiences to those we serve. As a premier hospitality organization and a certified Great Place to Work, we are committed to redefining private air travel. Our nearly 6,000-strong team of aviation experts and enthusiasts is dedicated to delivering excellence to our guests and communities, and it starts with taking care of our team. Signature provides a variety of benefits, programs, and resources to support our team members’ overall well-being and professional development. We proudly volunteer and give back, focusing on elevating the neighborhoods where we operate, empowering the next generation of aviation professionals, and supporting our veterans. From your health to your financial wellness, there are several benefits for you and your family when joining Signature Aviation. Our Benefits:Medical/prescription drug, dental, and vision InsuranceHealth Savings AccountFlexible Spending AccountsLife InsuranceDisability Insurance401(k)Critical Illness, Hospital Indemnity and Accident InsuranceIdentity Theft and Legal ServicesPaid time offPaid Maternity LeaveTuition reimbursement Training and DevelopmentEmployee Assistance Program (EAP) & Perks Qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, or other protected characteristics. Minimum Education and/or Experience:10+ years of experience in cloud engineering, platform engineering, or infrastructure architecture rolesStrong experience designing and operating containerized environments using Kubernetes and distributed systems architecturesExperience supporting infrastructure for AI, machine learning, or advanced data platformsExperience supporting AI model deployment or inference platforms used in operational environmentsStrong knowledge of cloud networking, security architecture, and reliability engineering practicesBachelor’s degree in Computer Science, Engineering, or a related technical fieldPreferred QualificationsExperience supporting LLM platforms, vector databases, and modern AI application architecturesFamiliarity with frameworks and tools such as LangChain, LlamaIndex, or Semantic KernelExperience integrating with foundation models such as OpenAI GPT, Claude, LLaMA, or GeminiExperience supporting predictive analytics and data science platforms in production environmentsFamiliarity with authentication and security frameworks such as OAuth2 and RBACExperience supporting multimodal AI systems, including vision, speech, or structured data processingExperience designing infrastructure for edge computing or field-based AI deploymentsFamiliarity with aviation, logistics, mobility, or other operational technology environmentsExperience working in regulated industries such as aviation, logistics, finance, or hospitalityDesign and implement cloud infrastructure supporting LLM platforms, vector databases, and model inference pipelinesBuild and operate scalable environments supporting agentic AI systems, predictive models, and enterprise AI applicationsSupport AI-driven operational use cases such as dynamic pricing, demand forecasting, and ramp capacity optimizationImplement and maintain MLOps pipelines supporting model training, deployment, monitoring, and lifecycle managementDevelop Infrastructure-as-Code environments using tools such as Terraform to enable scalable and repeatable deploymentsOptimize cloud performance, scalability, and reliability for AI and data workloadsImplement monitoring, logging, and observability platforms to ensure operational visibility and system performanceCollaborate with security and compliance teams to ensure data protection, platform security, and regulatory complianceEnforce cloud governance, cost optimization, and operational resilience best practicesDesign and support infrastructure for edge computing solutions that enable AI capabilities in field operations environmentsPartner with engineering, data science, and platform teams to ensure seamless integration between AI infrastructure and enterprise systemsFull timePosting Date: 2026-04-02