Lead Cloud AI Platforms Engineer
Signature Aviation
full-time
Posted on:
Location Type: Hybrid
Location: Orlando • Florida • United States
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Job Level
About the role
- 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
Requirements
- 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
- 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
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
cloud engineeringinfrastructure architectureMLOpsInfrastructure-as-CodeKubernetesAI model deploymentcloud networkingreliability engineeringpredictive analyticsedge computing