Education&Experience
We look for a technical lead who sets the engineering direction for agentic AI while still building, and combines that with a business-first mindset:
- Curiosity above all: you dig into problems, question assumptions and want to understand how the airline actually works.
- A business-first, human-centric mindset: aviation is made for humans, by humans, and AI supports people, it does not replace them. Fluent English, comfortable in a culturally diverse, international team.
- 8+ years building production-grade software, including 4+ years with GenAI, LLMs and applied ML and at least 1 year of hands-on agentic AI as an early adopter, with a track record of setting technical direction and shipping agentic systems at scale.
- Hands-on experience or strong working knowledge of MCP (Model Context Protocol) for connecting agents to tools, systems, APIs and data.
- Strong Python, with strong knowledge of at least one of TypeScript / JavaScript, Java, or C#, and modern engineering practice: async programming, FastAPI, Pydantic, Git and CI/CD, testing, error handling and logging.
- Practical experience with at least one agent framework or enterprise AI platform (e.g. LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, Microsoft Foundry, Amazon Bedrock AgentCore, Google Vertex/Gemini) and with a vector database or search platform (e.g. Azure AI Search, pgvector, Pinecone, Weaviate, OpenSearch).
- Experience integrating enterprise systems (APIs, managed identities, webhooks, queues, middleware) and deploying on cloud with containers, monitoring and observability; sound judgement on the trade-offs of latency, quality, cost and reliability, and on security, privacy, responsible AI and governance.
- Strong assets: aviation or airline domain knowledge; a background in classical machine learning and data science; and classical full-stack development (interfaces, frontends, APIs, backend engineering).
- Preferred for this level:
- Experience building AI agents for complex enterprise or operations-heavy workflows (logistics, supply chain, aviation, cargo, customer operations or contact centre).
- Experience with voice AI, email automation, CRM integrations, workflow automation or multilingual agents.
- Experience designing golden test sets, simulation-based testing, regression testing and agent evaluation frameworks.
- Experience designing multi-agent systems and agent-to-agent communication patterns, agent registries or tool-orchestration standards.
- Experience leading delivery with external AI platforms, startups or vendors while building internal engineering capability.
- Master's degree in Computer Science, Software Engineering, Data Science, AI/ML or a related technical field, or equivalent practical experience; relevant cloud-AI, GenAI, agentic-AI or MLOps certifications are an advantage.
Growth path: the natural next step is AI Value Architect, owning a cluster's value journey while still building alongside the team.
