Understand before you build. Start every problem with the business need and the why, working directly with stakeholders, then take the solution from discovery to production. - Design, build, deploy and continuously improve enterprise-grade agentic AI applications for real aviation scenarios, using agentic coding as your default way of working.
- Build agents that reason across steps, call tools and APIs, manage context, handle exceptions and support human-in-the-loop, reliably and at enterprise scale.
- Design and implement RAG pipelines over enterprise knowledge: ingestion, chunking, embeddings, vector search, retrieval tuning, grounding and source traceability.
- Build MCP-based integrations and connect agents to backend systems via REST/OpenAPI, webhooks and event-driven patterns with secure authentication, and expose your own work as clean, reusable, self-serviceable interfaces.
- Apply structured LLM patterns end to end: tool calling, schema-validated outputs, retries, fallbacks and guardrails.
- Own quality from day one: testing, evaluation, observability, logging, versioning and feedback loops for reliability, accuracy, latency, security and cost.
- Apply security, privacy, access control, auditability, responsible-AI and governance across every deployment.
- Take single-threaded ownership of a domain outcome (one owner, one result), and help establish reusable patterns that grow Etihad's internal AI capability rather than renting it.
- Coordinate with your Business Product Owner, AI Value Architect and other squads; speak up when AI is not the right tool.
- Set the technical direction and standards for the squad's agentic AI work, and make the key architecture and build-vs-buy calls.
- Design multi-agent and agent-to-agent systems and the evaluation frameworks that keep them reliable, and lead delivery with external AI platforms and vendors while building Etihad's internal capability.
- Grow the engineers around you: mentor, review, and raise the bar on quality, security and cost across the squad.
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.
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