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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level, metro role with a popular ML/AI title and hybrid remote increases applicant competition.
Core ML/AI skills transfer across industries, but airline domain and enterprise SaaS experience increase specificity.
Explicit 3–5 years plus mandatory LLM, MLOps and SageMaker requirements make screening highly selective.
Job Description
Structured overview of role & requirementsAbout This Role
Design and develop production-ready ML models focused on airline domain applications such as revenue optimisation, demand forecasting, and offer personalisation.
Build and maintain end-to-end ML pipelines including data ingestion, model training, evaluation, and deployment using AWS SageMaker.
Lead MLOps implementation including model monitoring, drift detection, automated retraining, and mentor junior AI engineers.
Minimum Requirements
3–5 years of ML/AI engineering experience with production model deployment.
Expertise in Python ML programming and experience with LLMs, RAG, and generative AI techniques.
Experience with MLOps and AWS SageMaker or equivalent production ML infrastructure platforms.
Bachelor's degree in Computer Science, Mathematics, or related field.
Ideal Candidate Profile
Strong domain knowledge or demonstrated curiosity in airline industry AI applications, including revenue management and pricing optimisation.
Proven ability to translate complex airline business problems into ML solutions, collaborating with product and data teams.
Experience applying advanced generative AI methods such as LLM fine-tuning and prompt engineering in production environments.