AIML Data Engineer

Are you passionate about building the data infrastructure that powers AI-driven customer experience measurement and using that data to uncover the why behind the numbers The AppleCare Customer Insights (ACCI) team is redefining how Apple measures and improves generative support experiences. Our AIML initiatives use large language models to evaluate support conversations across multiple quality dimensions providing real-time signal to leadership on how our AI-powered support is performing. We are seeking an AIML Data Engineer to own the data pipelines feature engineering and telemetry infrastructure that underpin our AIML portfolio while also serving as a hands-on analytics partner who conducts root cause analysis targeted investigations and data-driven deep dives that translate pipeline outputs into actionable insights for program managers and leadership.

The AIML Data Engineer builds and maintains the data foundation that powers ACCIs AI/ML initiatives and turns that foundation into insight. You will design and implement pipelines that ingest support conversation data transform it into model-ready formats orchestrate scoring workflows and deliver telemetry then go a step further by partnering with program managers to investigate trends diagnose performance shifts and surface the stories in the data that drive decisions. This is a full-stack engineering-and-analytics role that consolidates data pipeline orchestration model feature engineering telemetry analytics and investigative analysis into a single high-impact position.

Data Engineering u0026 Infrastructure:nDesign build and maintain scalable data pipelines that ingest transform and deliver support interaction data to LLM-based scoring systemsnEngineer features and data transformations that prepare conversation data for AI consumption metadata enrichment schema normalization and prompt context assemblynBuild and maintain telemetry pipelines that track model performance scoring accuracy and concept drift across LLM-based auto evaluation dimensionsnDevelop and operate pipeline orchestration workflows ensuring reliable timely data delivery across multiple AIML workstreamsnImplement data quality checks validation and monitoring at pipeline ingestion pointsnBuild monitoring and alerting for pipeline health data freshness scoring latency and load failuresnIntegrate with upstream data sources (Snowflake enterprise support systems) and downstream consumers (dashboards executive reporting model retraining)nnAnalytics u0026 Investigation:nConduct root cause analysis when CXI scores CSAT or other metrics shift diagnosing whether changes are data-driven model-driven or reflect real customer experience changesnPartner with program managers on targeted investigations: identifying cohorts isolating variables and quantifying impact of specific support experiencesnProactively surface anomalies trends and opportunities from pipeline telemetry before they become escalations

Bachelors degree in Computer Science Data Science Statistics Engineering or related field (or equivalent experience)n4 years of experience in data engineering or analytics engineeringnStrong proficiency in SQL and Python for both data engineering and analytical investigationnExperience with cloud data platforms (Snowflake Databricks or similar)nExperience with ETL/ELT tools and pipeline orchestration (dbt Airflow Prefect or similar)nDemonstrated ability to conduct root cause analysis and translate data findings into actionable recommendationsnExperience with version control (Git) and CI/CD practices

Experience building data pipelines supporting LLM-based systems (RAG scoring evaluation)nExperience with data visualization and storytelling (Tableau Streamlit or similar)nFamiliarity with NLP data preparation tokenization embedding generation prompt engineering data flowsnExperience with streaming or event-driven data architectures (Kafka or similar)nExperience with data quality and observability tools (Great Expectations Monte Carlo or similar)nUnderstanding of concept drift detection and model monitoring pipelinesnExperience supporting program or product teams with investigative analytics in a customer experience domain