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 (e.g., 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:
- Develop 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.
