Azure AI Platform Engineer

NTT DATA North America is a leader in IT and business services, and they are seeking an Azure AI Platform Engineer to develop AI/ML applications focused on prediction, recommendation, and text analytics. The role involves collaborating with cross-functional teams to implement GenAI solutions, create data infrastructure, and ensure client satisfaction through customized AI solutions.

Responsibilities:

• Apply statistical skills and advanced statistical techniques and concepts
• Demonstrate deep knowledge of ML frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn
• Leverage advanced knowledge of Python open-source software stack such as Django or Flask, Django Rest or FastAPI, etc
• Deep knowledge in statistics and Machine Learning models, deep learning models, NLP, Generative Adversarial Networks (GAN), and other generative models
• Experience working with RAG technologies and LLM frameworks, LLM model registries (Hugging Face), LLM APIs, embedding models, and vector databases
• Employ technical knowledge and hands-on experience with Azure OpenAI, Google Vertex Gen AI, and AWS LLM foundational models, BERT, Transformers, PaLM, Bard, etc
• Display proficiency in programming languages such as Python and understanding of various Python packages. Experience with TensorFlow, PyTorch, or Keras
• Develop and implement GenAI solutions, collaborating with cross-functional teams, and supporting the successful execution of AI projects for a diverse range of clients
• Assist in the design and implementation of GenAI use cases, projects, and POCs across multiple industries
• Work on RAG models and Agents Frameworks to enhance GenAI solutions by incorporating relevant information retrieval mechanisms and frameworks
• Create and maintain data infrastructure to ingest, normalize, and combine datasets for actionable insights
• Work closely with customers to understand their requirements and deliver customized AI solutions
• Interact at appropriate levels to ensure client satisfaction and project success
• Communicate complex technical concepts clearly to non-technical audiences
• Conduct training sessions to enhance overall data science skills within the organization

Requirements:

• 7+ years of experience with Azure architecture and Azure Kubernetes
• 3+ years of experience with AI platform engineering, ModelOps
• Apply statistical skills and advanced statistical techniques and concepts
• Demonstrate deep knowledge of ML frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn
• Leverage advanced knowledge of Python open-source software stack such as Django or Flask, Django Rest or FastAPI, etc
• Deep knowledge in statistics and Machine Learning models, deep learning models, NLP, Generative Adversarial Networks (GAN), and other generative models
• Experience working with RAG technologies and LLM frameworks, LLM model registries (Hugging Face), LLM APIs, embedding models, and vector databases
• Employ technical knowledge and hands-on experience with Azure OpenAI, Google Vertex Gen AI, and AWS LLM foundational models, BERT, Transformers, PaLM, Bard, etc
• Display proficiency in programming languages such as Python and understanding of various Python packages
• Experience with TensorFlow, PyTorch, or Keras
• Develop and implement GenAI solutions, collaborating with cross-functional teams, and supporting the successful execution of AI projects for a diverse range of clients
• Assist in the design and implementation of GenAI use cases, projects, and POCs across multiple industries
• Work on RAG models and Agents Frameworks to enhance GenAI solutions by incorporating relevant information retrieval mechanisms and frameworks
• Create and maintain data infrastructure to ingest, normalize, and combine datasets for actionable insights
• Work closely with customers to understand their requirements and deliver customized AI solutions
• Interact at appropriate levels to ensure client satisfaction and project success
• Communicate complex technical concepts clearly to non-technical audiences
• Conduct training sessions to enhance overall data science skills within the organization