Quest Global Recruitment 2023 | AI ML Engineer | BE/ B.Tech

Quest Global Recruitment 2023 | AI ML Engineer | BE/ B.Tech – CSE/ IT/ ECE | Bangalore

Company: Quest Global Engineering Pvt Ltd
Quest Global Recruitment 2023: Quality Engineering & Software Technologies Pvt. Ltd. (QuEST) is a leading provider of engineering solutions for advanced technology products in the Aerospace, Automotive, Power Generation, Oil & Gas, and Industrial Product domains.

For 25 years, we have strived to be the most trusted partner for the world’s hardest engineering problems. As a global organization headquartered in Singapore, we live and work in 17 countries, with 56 global delivery centers, driven by 17,000+ extraordinary employees who make the impossible possible every day.

Company Website: www.quest-global.com

Positions: AI ML Engineer

Experience: 3 – 6 years

Job Location: Bangalore

Salary: Best in Industry

Eligibility Criteria for Quest Global Recruitment 2023:

BE/ B.Tech
3-6 years of experience

Job Description:

We are looking for humble geniuses, who believe that engineering has the potential to make the impossible, possible; innovators, who are not only inspired by technology and innovation, but also perpetually driven to design, develop, and test as a trusted partner for Fortune 500 customers.

As a team of remarkably diverse engineers, we recognize that what we are really engineering is a brighter future for us all. If you want to contribute to meaningful work and be part of an organization that truly believes when you win, we all win, and when you fail, we all learn, then we’re eager to hear from you.

Job Responsibilities:

Proficiency with Python and basic libraries for machine learning.
Strong experience with object-oriented/object function scripting languages: Python
To research, modify, and apply data science and data analytics prototypes.
To create and construct methods and plans for machine learning.
Employing test findings to do statistical analysis and improve models.
To search internet for training datasets that are readily available.
ML systems and models should be trained and retrained as necessary.
To improve and broaden current ML frameworks and libraries.
To create machine learning applications in accordance with client or customer needs.
To investigate, test, and put into practice appropriate ML tools and algorithms.
To evaluate the application cases and problem-solving potential of ML algorithms and rank them according to success likelihood.