Data Scientist

1 week ago


Delhi, Delhi, India Brillio Full time

About Brillio:

Based out of Silicon Valley, Brillio, a leading technology consulting and services company, continues to be at the forefront of data science innovation in 2023.

Leveraging cutting-edge technology and advanced analytics, Brillio empowers businesses with data-driven insights to make informed decisions, optimize operations, and achieve remarkable outcomes.

With a strong focus on machine learning, data analytics, and artificial intelligence, Brillio is helping organizations across diverse industries transform their data into a strategic asset.

Their commitment to innovation and a client-centric approach positions Brillio as a trusted partner for unlocking the full potential of data science, ultimately driving growth and competitiveness.

Brillio is seeking a highly skilled and experienced Senior Data Scientist with expertise in Natural Language Processing (NLP), Generative AI, Machine Learning (ML), and Deep Learning (DL).

As a key member of our Data Science team, you will play a pivotal role in developing state-of-the-art solutions for our clients and driving innovation in the field.

Must-HaveStrong experience in Python, Docker & Kubernetes, Machine Learning, Deep Learning and Generative AI.Stay abreast of the latest advancements in machine learning, natural language processing, and generative modeling.

Conduct experiments to improve model architectures, training techniques, and generation capabilities.
Design and develop novel algorithms and architectures for enhancing the performance and efficiency of LLMs.
Optimize existing models for scalability, speed, and resource utilization.
Collaborate with cross-functional teams to integrate LLMs into production systems effectively.
Conduct thorough experimentation and validation to measure the performance of LLMs across various tasks and domains.
Proficiency in programming languages such as Python, along with expertise in deep learning frameworks like TensorFlow or PyTorch.
Proficient in deploying LLMs in production.
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