What you’ll achieve:

You will collaborate with other Gen AI engineers, ML engineers, Data Scientists and our marketing partners to participate in building intelligent solutions that transform how we engage with customers.  You will be part of a high-impact initiative leveraging Agentic AI and LLMs to deliver personalized guidance to our sales teams – empowering them to connect our customers with the right solutions.

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You will:

• Support the development of prompt and agentic workflows, machine learning models, and automation experiments by collaborating with experienced team members. Contribute to building insights dashboards and learn about deploying models and workflows to production environments.
• Assist in creating scalable solutions that span multiple geographies, while learning how to balance performance and customer experience in real-world applications.
• Work closely with industry-leading professionals in a collaborative environment, gaining hands-on experience and mentorship to help you grow and explore your own path.
• Explore open-source technologies and contribute to innovative, scalable solutions. Be encouraged to think creatively and flexibly as you apply AI/ML methodologies to real-world problem statements.
• Contribute to improving algorithms and enhancing customer experience, learning how to deliver the right product, message, and timing through data-driven insights and experimentation.

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Essential Requirements

• Daily University student in the field of Artificial Intelligence, Statistics, Computer Science, Mathematics or other related areas.
• Knowledge of predictive modeling and / or Agentic AI, LLMs.
• Knowledge of Python and SQL (especially on data wrangling, analysis, and modeling) plus code versioning tools like Git.
• Problem solving, analytic skills, and written and oral English communication skills
• Curiosity and interest in Gen AI, LLMs, NLP

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Desirable Requirements

• Familiarity  with State Of The Art techniques and models in the AI space
• Familiarity on the theory around standard ML methods is a plus: e.g., linear / logistic / regularized regressions, nearest neighbors, k-means, DB scan, collaborative filtering, random forests, xgboost, light gbm, SVM, neural networks, deep neural networks, deep belief networks.