AI ML Engineer

How You'll Help Us Keep Climbing (Overview & Key Responsibilities)

• Work with business and technology stakeholders to understand and translate opportunities and requirements into well-defined AI problems.
• Develop production-grade AI services, APIs, microservices, and integrations using sound software engineering principles.
• Build solutions using appropriate techniques such as generative AI, natural language processing, retrieval, classification, prediction, recommendation, or optimisation.
• Develop AI application workflows that may include prompting, structured outputs, tool use, agent orchestration, search, retrieval-augmented generation, and conversational context.
• Create and maintain data ingestion, preparation, indexing, retrieval, and model-serving pipelines where required.
• Define evaluation approaches using representative datasets, automated tests, quality metrics, human review, and business outcomes.
• Deploy, monitor, and support AI solutions in cloud environments, balancing quality, reliability, latency, scalability, and cost.
• Investigate production issues using logs, traces, metrics, evaluation results, and user feedback, and drive improvements through the full product lifecycle.
• Integrate AI capabilities with enterprise systems, external services, and existing technology platforms.
• Contribute to technical documentation, testing, CI/CD practices, and reusable engineering patterns.
• Clearly communicate assumptions, limitations, risks, uncertainty, and technical trade-offs to technical and non-technical audiences.
• Develop an understanding of the airline and travel domain and how technology supports customers, colleagues, and operations.
  
  

What You Need To Succeed (Minimum Qualifications)

• Bachelor's degree in computer science, engineering, data science, mathematics, or a related discipline, or equivalent practical experience.
• Three or more years of professional experience in software engineering, applied machine learning, data science, or AI engineering, including hands-on Python development.
• Good software engineering fundamentals, including modular design, version control, testing, and debugging.
• Experience developing and operating APIs, microservices, or backend applications using a modern Python web framework.
• Hands-on experience delivering solutions using large language models, natural language processing, machine learning, or related AI technologies.
• Understanding of common AI application patterns such as prompting, structured outputs, tool integration, embeddings, semantic search, retrieval, and conversational workflows.
• Knowledge with one or more LLM platforms, AI application frameworks, model SDKs, or model-serving approaches.
• Knowledge on deploying and supporting containerised production workloads on AWS, Azure, or another major cloud platform.
• Working knowledge of CI/CD, DevOps or DevSecOps practices, observability, and production incident investigation.
• Understanding of data privacy, security, responsible AI, and the operational risks associated with probabilistic systems.
  
  

We value engineers who can reason about the problem, select appropriate techniques, and take ownership of production outcomes rather than simply apply a particular framework.

Behavioral Competencies

• Ability to work in collaborative environments and embrace diverse perspectives.
• Communicate clearly and concisely, express thoughts and ideas effectively, and embrace cultural differences with respect when engaging with others.
• Ability to engage effectively with peers and stakeholders to build strong partnerships.
• Prioritize, maintain focus, and consistently deliver commitment.
• Proactively understand customer expectations and willingness to create customer-based solutions.
  
  

What Will Give You a Competitive Edge (Preferred Qualifications)

• Master's degree in computer science, mathematics, artificial intelligence, machine learning, or a related field.
• Experience with retrieval-augmented generation, enterprise search, document ingestion, vector search, or knowledge-management solutions.
• Knowledge on conversational AI, real-time voice, streaming, speech-to-text, or text-to-speech systems.
• Knowledge on model and AI application observability, evaluation platforms, experimentation, or quality monitoring.
• Experience with asynchronous processing, distributed systems, messaging, relational or NoSQL databases, and caching technologies.
• Knowledge on guardrails, content-safety controls, prompt-injection protections, and sensitive-data handling to AI applications.
• Experience integrating AI products with enterprise platforms and third-party services.
• Knowledge of airline, travel, retail, customer-service, or another complex customer-facing domain.