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High due to strong Airbus brand, mid-level ML role, metro location, and popular AI skillset.
Medium because core ML skills transfer widely but aerospace and knowledge-graph experience favor domain specialists.
High due to explicit 3+ years requirement and mandatory ML frameworks plus domain technical needs.
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Own end-to-end lifecycle of machine learning models from design, optimization to production deployment and monitoring.
Develop and deploy scalable ML/DL models including computer vision, NLP, and LLMs, and architect automated ML pipelines (MLOps).
Collaborate with Data Scientists and Product Managers to integrate AI capabilities and optimize model performance in cloud or edge environments.
3+ years of professional experience as ML Engineer, Data Scientist, or Software Engineer with focus on AI.
Bachelor's or higher degree in Computer Science, Data Science, Mathematics, or related quantitative field, or equivalent practical experience.
Strong proficiency in Python (and/or Go) with production-grade coding standards; expertise in ML frameworks like PyTorch, TensorFlow/Keras and libraries like Scikit-Learn, Pandas, NumPy.
Solid understanding and experience in computer vision techniques and frameworks, and familiarity with ML lifecycle tools such as MLFlow, DVC, Spark, Kafka, and data platforms.
Experience working in multi-functional, agile environments bridging software engineering and data science to deliver digital solutions.
Strong hands-on experience with cloud platforms (AWS, GCP, Azure) and expertise in deploying and optimizing AI/ML models with real-time production constraints.
Competency in advanced AI technologies including large language models, knowledge graphs, and modern MLOps practices, with a portfolio demonstrating practical AI/ML project contributions.