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Mid-level ML role in Mumbai with common LLM/MLOps skills and metro location causing moderate applicant density.
Strong ML skills transferable but airline domain knowledge preferred, creating moderate industry bias.
Explicit 3–5 years requirement plus mandatory MLOps, SageMaker, LLM and Python skills make filters stringent.
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Design and develop production-ready machine learning models focused on airline domain applications such as revenue optimization, demand forecasting, and offer personalization.
Build and manage end-to-end ML pipelines including data ingestion, model training, evaluation, and deployment using AWS SageMaker.
Lead MLOps processes including model monitoring, drift detection, automated retraining, and mentor junior AI engineers on ML best practices.
3–5 years of experience in ML/AI engineering with production model deployment.
Expert-level skills in Python ML, experience with large language models (LLMs), retrieval-augmented generation (RAG), and generative AI.
Experience with MLOps practices and production ML infrastructure, including AWS SageMaker.
Bachelor's degree in Computer Science, Mathematics, or related field.
Demonstrated experience applying LLMs, RAG, and generative AI techniques in enterprise software, especially with prompt engineering and fine-tuning.
Domain knowledge or strong curiosity about airline-specific AI applications such as revenue management and pricing optimization.
Technical proficiency with AWS SageMaker and familiarity with airline software ecosystem players like FLYR, Amadeus, and PROS.