Data Scientist with Gen AI (Azure Data Bricks)

Data Scientist with Gen AI (Azure Data Bricks)

Santa Clara,United States

Contract

Skills

Gen AI

Data Scientist

Azure

databricks

Python

•              B.E./ B. Tech / M. Tech/ MCA in computer science, artificial intelligence, or a related field

•              Experience with Data Science (AI/ML)

•              Strong programming skills in Python

•              Experience with deep learning frameworks (e.g., TensorFlow, PyTorch)

•              Hands-on AI/ML modeling experience of complex datasets combined with a strong understanding of the theoretical foundations of AI/ML(Research Oriented).

•              Expertise in most of the following areas: supervised & unsupervised learning, deep learning, reinforcement learning, federated learning, time series forecasting, Bayesian statistics, and optimization.

•              Hands-on experience on design, and optimizing LLM, natural language processing (NLP) systems, frameworks, and tools.

•              Building RAG application independently using available open source LLM models.

•              Comfortable working in the cloud and high-performance computing environments (e.g., AWS/Azure/GCP, Databricks).


Please share following response from candidate-

Data Scientist

•              Years of experience in Machine Learning ?

•              Do you have experience in Deep Learning ? if yes, how many years?

•              Which Deep learning Framework have you worked with ? how do you rate yourself in it out of 10 ?

•              Have you trained or finetuned a deep learning model ? if finetuned, name few pretrained models you have finetuned?

•              Have you built NLP models? What specific NLP tasks have you tackled (e.g., sentiment analysis, named entity recognition, text summarization)?

•              Which NLP libraries or frameworks have you used (e.g., NLTK, spaCy, Hugging Face Transformers)?

•              Have you worked on CV projects? What types of tasks (e.g., object detection, image segmentation) have you handled?

•              Which CV architectures or pre-trained models have you utilized (e.g., CNNs, ResNet, YOLO)?

•              Do you have experience/exposure on working with LLM ? if yes, Which LLM have you used ( e.g., GPT3.5, Gemini, Llama)

•              AIML experience using unstructured data : XX(In Years)

•              How many models deployed in production which are consumed by end users: XX (Numbers of model)

•              Unstructured data models