Principal AI Agentic System Engineer

Responsibilities

• Arcitect scalable agentic AI systems that enable intelligent automation and decision support across enterprise operations
• Design and implement AI solutions supporting dynamic pricing and revenue optimization initiatives
• Develop predictive systems that improve ramp capacity planning, operational throughput, and workforce utilization
• Engineer LLM-powered applications using retrieval-augmented generation (RAG), agent frameworks, and orchestration platforms
• Design AI-driven automation solutions that streamline operational workflows and task management
• Build and maintain pipelines that enrich enterprise datasets, including crew, customer, and CRM data platforms
• Integrate AI capabilities with enterprise platforms and operational systems
• Establish engineering standards and architectural patterns for AI and LLM-based application development
• Ensure responsible AI implementation, including governance, monitoring, and security considerations
• Provide technical leadership, architectural guidance, and mentorship to engineering teams building AI capabilities

Qualifications

• Minimum Education and/or Experience: 10–12+ years of experience in software engineering, distributed systems, or AI/ML platform development
• Demonstrated experience building and deploying production-grade LLM applications or agent-based AI systems
• Strong expertise in cloud-native architecture and scalable platform design
• Experience integrating AI systems with enterprise data and operational platforms
• Bachelor’s degree in Computer Science, Engineering, or a related technical field (advanced degree preferred)
• Experience with LLM application frameworks such as LangChain, LlamaIndex, or Semantic Kernel
• Experience integrating with foundation models such as OpenAI GPT, Claude, or LLaMA-based systems
• Knowledge of vector databases such as Pinecone, Weaviate, or Redis Vector
• Strong programming experience with Python and TypeScript
• Experience with enterprise AI platforms including Azure AI, AWS Bedrock, or Google Vertex AI
• Experience with model lifecycle management and MLOps platforms such as MLflow or Azure ML
• Familiarity with AI observability tools such as Langfuse or Weights & Biases
• Experience implementing enterprise authentication and security patterns such as OAuth2 and RBAC
• Experience in aviation, logistics, mobility, or other large-scale operational environments preferred