• As a Principal Data Scientist with specialization in GenAI and Agentic AI, your role will be pivotal in working with the Engineering and DS teams to formulation of strategies and roadmaps for the design, development, and deployment of AI/ML, NLP, and GenAI models, ensuring their seamless transition into production environments and guaranteeing their reliability and scalability.
• You should be working on cutting-edge large language models (LLMs) by leveraging Walmart's vast datasets and working with a team of Data scientists and engineers in solving intricate AI/ML challenges through research and development, pushing the limits of innovation and making groundbreaking contributions to the field.
• Overseeing the entire lifecycle of projects, from inception and data collection to model prototyping and deployment, while effectively managing stakeholder relationships and facilitating cross-functional communication.
• Collaborating extensively with product and engineering leaders to accelerate innovations in discovery experiences, utilizing insights, frameworks, machine learning prototypes, and emphasizing the strategic importance of AI initiatives.
• Adhering strictly to Walmart’s policies, procedures, mission, values, standards of ethics, and integrity.Designing end-to-end system architecture for GenAI/AI/ML and data-intensive applications, setting new benchmarks in the industry.
• Developing and deploying robust, production-grade real-time/batch machine learning services that set industry benchmarks.
• Collaborating with product managers to design user journeys, feedback loops and analyze user telemetry, thus creating seamless user experiences.
• Identifying or proposing innovative AI/ML use-cases to business teams to boost business processes and developing quick MVPs/POCs to enable stakeholders to make data-driven decisions.

What you'll bring :

• GenAI: understanding of usecases in developing and implementing AI models and algorithms.

• Python: Knowledge of Python, including libraries such as NumPy, Pandas, and Scikit-learn.
• Apache Spark: Experience with big data processing frameworks like Apache Spark.
• Scala: Knowledge of Scala for data processing and analysis.
• Machine Learning: Expertise in machine learning techniques and frameworks such as TensorFlow, Keras, and PyTorch.
• Data Science: Strong foundation in data science principles, including statistical analysis, data visualization, and data manipulation.
• Algorithms: Ability to design and implement efficient algorithms for data processing and analysis.
• Engineering: Experience in software engineering practices, including version control, testing, and deployment.