| ML Engineer in Santa Clara CA at Santa Clara, California, USA |
| Email: [email protected] |
| From: Sanjeev, Saransh, Inc. [email protected] Reply to: [email protected] Hi, Please let me know if you have any profiles for this role Role: Azure ML Engineer Location: Santa Clara, CA(Onsite) Need local Visa :: H1B, H4EAD, L2,L1,E3 Prior Experience Hands on experience on working in Azure cloud platform Experience in designing and implementing scalable and secure Azure cloud solutions using Data Bricks ML platform. Understanding of implementation of architecture within Azure Big Data Analytics Al tools. Expert level in Designing and Architect solutions in Azure Data factory, Azure Databricks, Azure Datalake, Delta Lake and Azure synapse analytics implementation. Good to have Experience in Azure cloud technologies like PySpark, Synapse, ADF, Databricks, Python, Scala and SQL. Have good experience configuring Microservices using Docker, Kubernetes on Azure Data Bricks Extensive Experience on working on Azure AI services including Data Bricks and Azure cognitive services Experience in creating and deploying code libraries using functions and classes in Python in AI product-focused development. Well-versed in software development and code quality best industry practices. Experience working with Agile Development methodologies. Responsibilities: Primary responsibility is to , design and develop AI application on Azure cloud and Databricks. Lead the azure cloud architecture design for various applications depending on business requirements Hands-on experience in one or all the following areas: LLM, NLP, DL (Deep Learning), ML (Machine Learning), object detection/classification, tracking, etc. Work with business users to translate functional requirements into data specifications Collaborate with data scientists and business users to transform domain know-how into functional requirements. Model Training and Evaluation: Train and fine-tune language models using appropriate machine-learning frameworks and tools. Research and Innovation: Stay up to date with the latest advancements in the field of natural language processing and machine learning. Collaboration and Communication: Collaborate effectively with cross-functional teams, including researchers, DevOps architects/engineers and project managers. Documentation and Knowledge Sharing: Document code, algorithms, and processes to ensure effective knowledge transfer and maintain codebase quality. Requirements: B.E./ B. Tech / M. Tech/ MCA (Computer Science/Electronics & Communication/Electrical), a master's in computer science, artificial intelligence, or a related field, or a specialization in natural language processing is preferred. 4-6 years of IT experience with a min of 3+ years in Azure Data Bricks for development and Azure Services. Strong Expertise in the Azure cloud solutions. Strong programming skills in Python. Familiarity with statistical, ML and DL techniques and models Hands-on experience in one or all the following areas: LLM, NLP, DL (Deep Learning), ML (Machine Learning), object detection/classification, tracking, etc. Experience with ML / DL frameworks (e.g., Sklearn, TensorFlow, PyTorch) is needed. Experience with large-scale data processing, including data collection, cleaning, and preprocessing. Proficiency in working with UNIX/Linux environments and command-line tools. Strong analytical and problem-solving abilities. Excellent communication skills, interpersonal, written, visual (presentations), and verbal. Certifications Databricks - Machine learning professional (highly preferred) Questionnaire : 1. Years of experience in Databricks Machine Learning 2. Have you deployed any models using Databricks model serving 3. Have you deployed a model with unstructured data using Databricks model serving Sanjeev M Saransh, Inc., 5 Independence Way, Suite # 225, Princeton, NJ 08540 [email protected] Keywords: artificial intelligence machine learning information technology Alabama California New Jersey ML Engineer in Santa Clara CA [email protected] |
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| Wed Jul 17 21:38:00 UTC 2024 |