Expert - Azure Data Scientist

Expert - Azure Data Scientist Job Summary

The Expert - Azure Data Scientist role at NTT1GLOBAL is focused on leveraging advanced statistical, machine learning, and deep learning techniques to drive innovation in generative AI and natural language processing. The position requires hands-on expertise in building and deploying ML models at scale using cloud platforms such as Azure, AWS, or GCP. The successful candidate will collaborate with cross-functional teams to understand business challenges, develop and optimise AI solutions, and ensure alignment with business objectives. This role is pivotal in enhancing the performance, scalability, and efficiency of generative AI models, supporting data-driven decision-making and business growth.

Responsibilities

• Implement generative AI models and identify insights that can be used to drive business decisions.
• Collaborate cross-functionally to understand business problems, develop hypotheses, and test those hypotheses with data.
• Work closely with multi-functional teams to define AI project requirements and objectives, ensuring alignment with overall business goals.
• Optimise existing generative AI models for improved performance, scalability, and efficiency.
• Lead efforts in the design and development of prompt engineering strategies and techniques to optimise the performance and output of GenAI models.
• Implement cutting-edge NLP techniques and prompt engineering methodologies to enhance the capabilities and efficiency of GenAI models.
• Take ownership of building and deploying ML models at scale in cloud environments such as Azure, AWS, or GCP.

Location

Location: Udupi, Karnataka, India

Skills

• Demonstrate a strong understanding of statistical methods, machine learning algorithms, and deep learning architectures.
• Show proficiency in Python and experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, or Keras.
• Possess familiarity with cloud-based platforms and services, including AWS, GCP, or Azure.
• Utilise natural language processing techniques and tools such as SpaCy, NLTK, or Hugging Face.
• Apply knowledge of model interpretability and fairness frameworks, including SHAP, LIME, and Fairlearn, as well as responsible AI principles.
• Demonstrate hands-on experience building and deploying ML models at scale in cloud environments, such as Azure ML, Vertex AI, or SageMaker.