ML Frameworks Engineer

at  Grid Dynamics

Our customer is an American multinational technology company headquartered in San Ramon, California. Our customer is one of the world’s largest technology companies based in Silicon Valley with operations all over the world. On this project, we are working with bleeding-edge big data technologies to develop a high-performance data analytics platform, which handles petabytes of data. Enhance fleet health data for evolving ML machine deployments through innovations like PRISM, SHAG, and Dark Sky. Your role involves refining big data pipelines, creating new ones, and ensuring code reliability to optimize spare part logistics and minimize shortages. Make a direct impact on the efficiency and reliability of our cutting-edge data center operations.

REQUIREMENTS:

• Strong understanding of machine learning principles, especially in the context of LLMs.
• 5+ years of proficiency in Python, including machine learning packages like Jax/Tensorflow or PyTorch.
• Skills in Java/scala (preferred).
• Experience building scalable deep learning systems.
• Experience with large-scale data infrastructure.
• Strong verbal and written communication skills with the ability to work effectively across internal and external organizations and virtual teams.
• BS/BA or equivalent degree in computer science or similar (preferred).

ABOUT US:

Grid Dynamics (Nasdaq:GDYN) is a digital-native technology services provider that accelerates growth and bolsters competitive advantage for Fortune 1000 companies. Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud migration, and application modernization. Grid Dynamics achieves high speed-to-market, quality, and efficiency by using technology accelerators, an agile delivery culture, and its pool of global engineering talent. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the US, UK, Netherlands, Mexico, and Central and Eastern Europe.
To learn more about Grid Dynamics, please visit www.griddynamics.com. Follow us on Facebook, Twitter, and LinkedIn

• Build machine learning tooling to facilitate various phases of the ML lifecycle from model training, data ETL, end-to-end model evaluation and deployment.
• Work with technical and non-technical stakeholders to build solutions to align LLMs for specific use cases.
• Deliver reusable and easy-to-use tooling to integrate with existing data and machine learning systems