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Mid-level ML/AI role in Mumbai with popular LLM/MLOps skills and hybrid flexibility.
Core ML and MLOps skills transfer across industries, but airline domain knowledge and LLM experience increase bias.
Explicit 3–5 years requirement plus mandatory LLM, MLOps and SageMaker experience raises strictness.
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Design and develop production-ready machine learning models and intelligent features focused on airline domain applications like revenue optimization and demand forecasting.
Build 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 on ML best practices.
3–5 years of experience in ML/AI engineering with production model deployment.
Expert Python skills and experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and generative AI.
Experience with MLOps and production ML infrastructure, specifically AWS SageMaker.
Bachelor's degree in Computer Science, Mathematics, or a related field.
Strong domain knowledge or curiosity about airline industry applications such as revenue management, demand forecasting, and pricing optimization.
Experience applying LLMs, generative AI, and RAG techniques to enterprise software products, with capabilities in prompt engineering and model fine-tuning.
Proficient operating in cloud-based AI environments, particularly AWS SageMaker and related AI services, with ability to bridge technical solutions and airline business problems.