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