To develop proprietary ML and AI solutions by leveraging state-of-the-art tools and frameworks, solving complex business challenges, and working extensively with cloud platforms and data management tools to build and deploy scalable AI models and solutions.

Making an Impact – Your Key Responsibilities, Accountabilities, Ways of Working :
• Develop and Deploy ML and AI Solutions: Design and implement advanced machine learning models, including deep learning frameworks like TensorFlow and PyTorch, to solve complex business problems.
• Manage Large Datasets: Efficiently handle large datasets, including data generation, augmentation, and preparation, using SQL, NoSQL, and other database technologies.
• Advance Natural Language Processing (NLP): Apply NLP techniques such as tokenization, text classification, and sentiment analysis for tasks involving text generation and processing.
• Optimize Model Performance: Fine-tune models by adjusting hyperparameters and employing optimization techniques to enhance performance and accuracy.
• Leverage Cloud Computing: Deploy and scale models Snowflake and Azure.
• Prompt Engineering: Develop effective prompts for LLMs to maximize the performance of Open AI, Llama 3.1, and others.
• Integrate AI Functionalities: Build AI-powered solutions by making API requests and integrating AI functionalities into applications.
• Drive Innovation: Pilot and scale new applications, including predictive modeling and advanced generative AI techniques.
• Communicate Insights: Translate complex technical concepts into actionable insights for non-technical stakeholders.
• Foster a Data-Driven Culture: Promote a data-driven culture through training, mentorship, and advocacy across the organization.
• Stay Current with Industry Trends: Keep up with the latest advancements in AI, machine learning, and data science to drive continuous innovation.

Your Typical Day and Other Key Details
• Collaborate with the Gen AI and data science team to design and implement machine learning models.
• Manage and prepare large datasets for analysis.
• Apply NLP techniques to various tasks.
• Optimize model performance through hyperparameter tuning and other techniques.
• Deploy and scale models on cloud platforms like Snowflake and Azure.
• Develop and test prompts for large language models.
• Integrate AI functionalities into applications via API requests.
• Communicate insights and findings to non-technical stakeholders.
• Stay updated on industry trends and advancements in AI and data science.

To develop proprietary ML and AI solutions by leveraging state-of-the-art tools and frameworks, solving complex business challenges, and working extensively with cloud platforms and data management tools to build and deploy scalable AI models and solutions.

Making an Impact – Your Key Responsibilities, Accountabilities, Ways of Working :
• Develop and Deploy ML and AI Solutions: Design and implement advanced machine learning models, including deep learning frameworks like TensorFlow and PyTorch, to solve complex business problems.
• Manage Large Datasets: Efficiently handle large datasets, including data generation, augmentation, and preparation, using SQL, NoSQL, and other database technologies.
• Advance Natural Language Processing (NLP): Apply NLP techniques such as tokenization, text classification, and sentiment analysis for tasks involving text generation and processing.
• Optimize Model Performance: Fine-tune models by adjusting hyperparameters and employing optimization techniques to enhance performance and accuracy.
• Leverage Cloud Computing: Deploy and scale models Snowflake and Azure.
• Prompt Engineering: Develop effective prompts for LLMs to maximize the performance of Open AI, Llama 3.1, and others.
• Integrate AI Functionalities: Build AI-powered solutions by making API requests and integrating AI functionalities into applications.
• Drive Innovation: Pilot and scale new applications, including predictive modeling and advanced generative AI techniques.
• Communicate Insights: Translate complex technical concepts into actionable insights for non-technical stakeholders.
• Foster a Data-Driven Culture: Promote a data-driven culture through training, mentorship, and advocacy across the organization.
• Stay Current with Industry Trends: Keep up with the latest advancements in AI, machine learning, and data science to drive continuous innovation.

Your Typical Day and Other Key Details
• Collaborate with the Gen AI and data science team to design and implement machine learning models.
• Manage and prepare large datasets for analysis.
• Apply NLP techniques to various tasks.
• Optimize model performance through hyperparameter tuning and other techniques.
• Deploy and scale models on cloud platforms like Snowflake and Azure.
• Develop and test prompts for large language models.
• Integrate AI functionalities into applications via API requests.
• Communicate insights and findings to non-technical stakeholders.
• Stay updated on industry trends and advancements in AI and data science.