About

MINIMUM QUALIFICATIONS

• * Master's degree in a quantitative discipline such as Statistics, Engineering,
• Sciences, or equivalent practical experience.
• * 3 years of experience in a data science role, with a specific focus on
• machine learning and Natural Language Processing (NLP) for developing and
• deploying AI/ML solutions.
• * Experience with relevant ML/AI libraries (e.g., TensorFlow, PyTorch,
• scikit-learn, Hugging Face).

PREFERRED QUALIFICATIONS

• * PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or
• a related quantitative field.
• * Experience with Large Language Models (LLMs), including their application in
• solving business problems.
• * Experience in intelligent autonomous agents, including their design,
• development, evaluation, and deployment.
• * Experience with cloud platforms (preferably Google Cloud Platform) and their
• AI/ML services, particularly those related to LLMs and generative AI.
• * Experience in Customer Support or Support-adjacent role.
• * Excellent programming skills in Python or a similar language with the ability
• to translate data into actionable insights and communicate findings to
• technical and non-technical stakeholders.

ABOUT THE JOB

• In this role, you will be instrumental in driving customer success at scale by
• building the predictive, personalized, and proactive solutions that define the
• future of customer support. You will work with datasets to develop and deploy
• innovative ML/AI solutions, translating data into actionable strategies.

RESPONSIBILITIES

• * Developing predictive, personalized, and proactive customer support solutions
• to drive customer success at scale while researching and integrating
• advancements in LLMs, generative AI, and AI agent architectures to
• continuously enhance our capabilities and foster innovation.
• * Lead the end-to-end development and deployment of advanced AI/ML solutions,
• with an emphasis on Large Language Models (LLMs) and intelligent autonomous
• agents, addressing business issues.
• * Implement evaluation frameworks and metrics for LLMs and AI agents,
• encompassing both traditional model performance and agent-specific evaluation
• criteria (eg. task completion rate, reasoning quality).
• * Monitor and maintain deployed LLM and AI agent solutions in production,
• including tracking key performance indicators, identifying and addressing
• model drift, and ensuring system stability and scalability.
• * Identify and define AI/ML opportunities by collaborating with stakeholders to
• translate business needs into technical requirements and measurable outcomes.