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Tier-1 brand, metro location, and a broad popular ML role with extensive skills requirements increase competition.
Role demands aerospace prognostics, RUL and PHM domain knowledge, making background fit highly sensitive.
Explicit 9+ years requirement and specialized prognostics/aviation ML expertise make shortlisting highly strict.
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Design and implement AI/ML solutions for aircraft Predictive Maintenance, including diagnostics, prognostics, and Remaining Useful Life (RUL) estimation.
Develop, productionize, and deploy machine learning models and scalable architectures for fleet-wide telemetry and real-time analytics.
Collaborate with cross-functional and global teams to deliver analytics solutions that improve aircraft performance and operational efficiency.
Bachelor’s degree in Engineering, Computer Science, Data Science, or related field.
Experience with data analytics, machine learning (regression, classification), and AI software systems.
Proficiency in Python (Pandas, NumPy, Scikit-Learn), deep learning frameworks (TensorFlow, PyTorch), SQL and NoSQL databases.
Work Experience Required: Typically 9+ years or equivalent combination of education and experience.
Experienced in failure diagnostics & prognostics in aviation, including anomaly detection, fault isolation, and Predictive Maintenance.
Familiarity with advanced AI/ML techniques such as LLMs, Retrieval-augmented generation, and explainable AI applied to aircraft systems.
Capable of handling end-to-end ML lifecycle (MLOps/DevOps) with strong cross-cultural collaboration skills in a global aerospace engineering environment.