Hi,
Hope you are doing well..
Job Title- Data Scientist
Location :Santa Clara ,CA (Onsite)
Only Local for CA
JD: • B.E./ B. Tech / M. Tech/ MCA in computer science, artificial intelligence, or a related field
• 6+ years of IT experience with a min of 3+ years in 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).
Basic Screening Questionnaire • 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
• Structure data Model
• DL framework : torch, Keras, Tensorflow
• Cloud platform – Azure (Data Bricks): xx (In years)
• Model build and deployed on cloud : XX (Number of models)
• Unstructured data models
• Structure data Model