Failure Mode/AI/ Product performance Engineer

Tata Technologies; Pune District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Tata Technologies in Pune, Maharashtra, invites a highly skilled data scientist to develop Python-based engineering data analytics and automation solutions. You will build ML/AI models for engineering applications, perform data cleaning, feature engineering, and statistical analysis, and visualize results to drive reliability improvements.

You will work on Generative AI, LLM and RAG use cases, and collaborate with cross-functional teams across Powertrain, Engine Systems, Auto Components and

Qualifications

• Strong hands-on experience in Python, ML, AI and data science.
• Experience in predictive modeling, feature engineering and statistical analysis.
• 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 or Engineering Services backgrounds will be preferred.

Responsibilities

• Develop Python-based solutions for engineering data processing, analytics and automation.
• Build ML/AI models for engineering applications.
• Data cleaning, feature engineering, analysis and visualization.
• Develop analytical solutions for failure prediction and reliability.
• Work on Generative AI, LLM and RAG-based use cases.
• Support Product Engineering, Design/Development, Manufacturing, Quality and Reliability Engineering activities.
• Analyze automotive, manufacturing, quality and product-performance datasets.
• Automate repetitive engineering analysis and reporting.
• 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.

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

Job description

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.