Azure ML Architect with Experience on Data Bricks Development platformSanta Clara, CA

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.
• 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. Highly preferred experience on working with Azure OpenAI services.
• Hands-on experience on design, and optimizing LLM, natural language processing (NLP) systems, frameworks, and tools.
• Hands-on AI/ML modeling experience of complex datasets combined with a strong understanding of the theoretical foundations of AI/ML.

Responsibilities:

• Your primary responsibility is to Architect, design and develop AI application on Azure cloud.
• Lead the azure cloud architecture design for various applications depending on business requirements.
• Prototypes and do proof of concepts (PoC) in one or all the following areas: LLM, NLP, DL (Deep Learning), ML (Machine Learning), object detection/classification, tracking, etc.
• Stay up to date with the latest advancements in LLM, NLP, deep learning, machine learning, and object detection algorithms, and proactively identify opportunities to leverage new technologies for improved solutions.
• You will work with a team of researchers and engineers to build state-of-the-art language models that can understand and generate human-like text.
• 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. Data Analysis and Preprocessing: Collect and preprocess large-scale text corpora to train and fine-tune language models.
• Conduct data analysis to identify patterns, trends, and insights that can inform model development and improvement.
• Language Model Development: Utilize off-the-shelf LLM services, such as Azure OpenAI, to integrate LLM capabilities into applications, and collaborate with the research team to design and develop state-of-the-art language models.

• 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.
• Communicate complex ideas and technical concepts clearly to stakeholders and team members supported by Large Language Model (LLM) technology.
• Documentation and Knowledge Sharing: Document code, algorithms, and processes to ensure effective knowledge transfer and maintain codebase quality.

Requirements:

• Specialization in natural language processing is preferred. 4-6 years of IT experience with a min of 3 years in Data Science (AI/ML) with a specialization in NLP and Azure Data Bricks for development and Azure Services. Strong Expertise in the Azure cloud solutions.
• Strong programming skills in Python. Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) is highly desirable.
• Solid understanding of natural language processing techniques, including language modeling, text classification, sentiment analysis, and machine translation.
• Familiarity with statistical and machine learning approaches, such as neural networks, recurrent neural networks (RNN), and transformer models.
• 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.