ML Ops / DevOps Engineer

Quest Global is an organization at the forefront of innovation and one of the world’s fastest growing engineering services firms with deep domain knowledge and recognized expertise in the top OEMs across seven industries. We are a twenty-five-year-old company on a journey to becoming a centenary one, driven by aspiration, hunger and humility.

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.

The achievers and courageous challenge-crushers we seek, have the following characteristics and skills :

Roles & Responsibilities :

We are looking for a highly motivated individual who can develop cutting edge MLOps and DevOps frameworks to deploy AI models. The candidate should have a solid grasp of state-of-the-art cloud technologies, best in class deployment architectures / frameworks and production grade software. The candidate must have an open mindset regarding the tradeoffs between coding from scratch versus utilizing existing frameworks. Many of the problems we encounter are novel and have never been solved before, so creative, out-of-the-box thinking and a fondness for experimentation are a must. We also want someone who stays current with recent trends in MLOps / DevOps so our approaches remain the most robust and competitive in the industry. Finally, the role requires strong team and interdisciplinary collaboration to see products through the development cycle from beginning to end

• Develop end-to-end ML pipelines encompassing the ML lifecycle from data ingestion, data transformation, model training, model validation, model serving, and model evaluation over time
• Collaborate closely with AI scientists to accelerate productionization of ML algorithms.
• Setup CI / CD / CT pipelines for ML algorithms.
• Deploy models as a service both on-cloud and on-prem.
• Learn and apply new tools, technologies, and industry best practices

Required Skills (Technical Competency) :