DATA SCIENTIST – ARTIFICIAL INTELLIGENCE (AI/ML + CAE)

Vrinda International; Bengaluru; On-site; INR 900,000 - 1,100,000; Full time

Job summary

Vrinda International is seeking a Data Scientist specializing in Artificial Intelligence (AI/ML) + CAE for Bengaluru, India. Experience 3–6 years. CTC up to ₹10 LPA. Work Mode: Work From Office.

Develop ML/DL models for engineering and automotive applications; identify problems for AI/ML, and build predictive/surrogate models to reduce simulation effort. Physics-guided ML and advanced architectures will be used to solve engineering challenges.

Qualifications

• Develop ML/DL models for engineering and automotive applications.
• Identify engineering problems suitable for AI/ML and predictive analytics.
• Build predictive/surrogate models to reduce simulation and computational effort.
• Develop physics-guided ML models for engineering applications.
• Apply suitable deep learning architectures based on business/engineering problems.
• Develop AI-driven optimization and DoE solutions.

Responsibilities

• Develop ML/DL models for engineering and automotive applications.
• Identify engineering problems suitable for AI/ML and predictive analytics.
• Build predictive/surrogate models to reduce simulation and computational effort.
• Develop physics-guided ML models for engineering applications.
• Apply suitable deep learning architectures based on business/engineering problems.
• Develop AI-driven optimization and DoE solutions.

Skills

AI optimization

Design of Experiments

Automotive AI/ML

Job description

DATA SCIENTIST – ARTIFICIAL INTELLIGENCE (AI/ML + CAE)

Job Description

HIRING | DATA SCIENTIST – ARTIFICIAL INTELLIGENCE (AI/ML + CAE)

Experience: 3–6 Years

CTC: Up to ₹10 LPA

Work Mode: Work From Office

Required Skills

• AI-driven Optimization & Design of Experiments (DoE)
• Engineering/Automotive AI/ML applications

Key Responsibilities

• Develop ML/DL models for engineering and automotive applications.
• Identify engineering problems suitable for AI/ML and predictive analytics.
• Build predictive/surrogate models to reduce simulation and computational effort.
• Develop physics-guided ML models for engineering applications.
• Apply suitable deep learning architectures based on business/engineering problems.
• Develop AI-driven optimization and DoE solutions.

Good to Have

• Engineering simulation & physics-based modeling knowledge