Failure Mode/AI/ Product performance Engineer

Role & Responsibilities:

• Develop Python-based solutions for engineering data processing, analytics and automation.
• Build and implement Machine Learning, AI and Data Science models for engineering applications.
• Perform data cleaning, feature engineering, statistical analysis, predictive analytics and data visualization.
• Develop analytical/predictive solutions for failure prediction, anomaly detection, product performance, quality improvement and reliability.
• Work on Generative AI, LLM and RAG-based engineering use cases.
• Work with engineering applications related to Powertrain, Engine Systems, Auto Components and Industrial Products.
• Support Product Engineering, Design/Development, Manufacturing, Quality and Reliability Engineering activities.
• Analyze automotive, manufacturing, quality, reliability and product-performance datasets.
• Automate repetitive engineering analysis and reporting activities.
• Perform data-driven Root Cause Analysis and translate analytical results into actionable engineering insights.
• Collaborate with cross-functional Automotive, Powertrain, Product Engineering, Manufacturing and Quality teams.

Required Skills: Python, Machine Learning, Data Science, AI, Generative AI, LLM, RAG, Predictive Analytics, Statistical Analysis, Feature Engineering, Data Visualization, Engineering Data Analytics, Automotive, Mechanical Engineering, Powertrain, Engine Systems, Auto Components, Product Engineering, Manufacturing Engineering, Quality Engineering, Reliability Engineering, Root Cause Analysis.

Preferred Candidate Profile:

• Strong hands-on experience in Python, Machine Learning, AI and Data Science.
• Experience in predictive modeling, feature engineering, statistical analysis and engineering data analytics.
• Exposure to Generative AI, LLM and RAG is preferred.
• Automotive/Mechanical domain knowledge with exposure to Powertrain, Engine Systems, Auto Components, Product Engineering, Manufacturing, Quality or Reliability.
• Candidates from Automotive, Auto Components, Industrial Products and Engineering Services backgrounds will be preferred.