About the position
Responsibilities
• Rapidly analyze and prototype AI-driven solutions that address key pain points for both customers using self-service systems and customer service (CS) agents interacting with internal tooling.
• Own and maintain production AI systems, including those leveraging large language models (LLMs); this includes optimization, fine-tuning, and debugging to ensure robustness and performance at scale.
• Bridge customer support needs with commerce platform capabilities, identifying which problems can be solved using existing infrastructure and which require new AI-powered solutions.
• Assess the effectiveness of deployed AI solutions through a data-driven lens, continuously iterating on what works and sunsetting or replacing what doesn't deliver meaningful impact.
• Collaborate with other AI-focused teams across Disney Streaming, sharing innovations and adopting best practices to drive consistency, scalability, and impact in AI initiatives.
Requirements
• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field; PhD is a plus.
• 2+ years of hands-on experience building, deploying, and evaluating LLM based solutions.
• Minimum 7 years of related work experience in building, deploying, and evaluating real-world machine learning solutions.
• Proficiency in Python, SQL, and other relevant programming languages.
• Experience with data wrangling and analysis tools such as Pandas, NumPy, and working knowledge of databases like MySQL and PostgreSQL.
• Familiarity with big data and workflow platforms, including Spark, Databricks, Snowflake, Airflow, Jupyter, and GitHub.
• Proven experience leading end-to-end ML project lifecycles, including model design, experimentation, product deployment, CI/CD for ML pipelines, and ongoing monitoring.
• Excellent problem-solving abilities, with strong communication and collaboration skills.
Nice-to-haves
• Experience working with Amazon Web Services.
• Experience with Tableau, Power BI, or similar platforms.
• Experience building anomaly detection models.
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