Back
Tier-1 brand, mid-level ML role, metro location, and broad skillset create high competition.
Specialized predictive-maintenance, PHM, and V&V requirements reduce industry transferability.
Explicit 4+ years, mandatory ML/MLOps stack and aviation V&V requirements increase strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and implement AI/ML solutions for predictive maintenance and health monitoring of aircraft systems to improve diagnostics, prognostics, and aircraft fleet operational efficiency.
Develop, productionize, and deploy machine learning models (anomaly detection, fault classification, Remaining Useful Life estimation) using modern ML frameworks and MLOps practices.
Collaborate with cross-functional global teams to translate AI/ML innovations into operational products, deliver explainable visualizations, and support AI testing, validation, and data governance.
Bachelor’s degree in Engineering, Computer Science, Data Science, Mathematics, Physics or equivalent relevant qualification.
Demonstrated experience in machine learning, AI models, and software systems with proficiency in Python and ML frameworks (TensorFlow, PyTorch).
Strong knowledge of classical ML algorithms, LLMs, Retrieval-augmented generation, vector DBs, semantic search, SQL and NoSQL databases.
Work Experience Required: Typically 4+ years related work experience or equivalent combination of education and experience.
Experienced in failure diagnostics, prognostics, or predictive maintenance, preferably in aerospace or related domains.
Skilled at applying advanced AI/ML techniques including deep learning, explainable AI, NLP, GenAI, and probabilistic modeling for complex system health monitoring.
Comfortable working in a cross-cultural, global engineering environment with ability to handle end-to-end ML lifecycle and interface with multiple stakeholders.