
Sr. Applied AI/ML Engineer
Role summary
Vi is seeking a Sr. Applied AI/ML Engineer to build and scale big-data and ML pipelines for their petabyte-scale data lakehouse. This hybrid role involves integrating customer data and packaging Vi's products into scalable, automated pipelines. Responsibilities include designing and implementing data/ML pipelines, prototyping predictive insights, and identifying reusable data capabilities. The ideal candidate has deep expertise in Apache Spark, Python, and AWS technologies for data engineering and ML, with strong communication skills for customer data integrations. Experience in healthcare/life sciences and familiarity with statistical modeling/experimental design are a plus.
Role Summary
Vi manages a petabyte scale data lakehouse that drives data and ML pipelines across all our products. Vi is looking for an engineer with deep expertise building and scaling big-data and ML pipelines. The role will be a hybrid between an FDE that owns integrations of customer data with Vi’s data lakehouse and a platform engineer responsible for packaging Vi’s products into repeatable, scalable, automated pipelines.
Tech Stack
- Apache PySpark, Apache Iceberg, AWS EMR, Glue, S3
- Python, PyTorch, sklearn, xgboost, catboost, mlflow
- AWS SageMaker, Airflow
Key Responsibilities
- Own the design and implementation of data and ML pipelines based on a combination of customer first party data and Vi’s internal data lakehouse.
- Prototype predictive insight products and run rapid feasibility studies to assess new commercial opportunities for Vi’s capabilities built on petabyte scale data lakehouse.
- Identify commonalities between customer engagements to synthesize reusable data capabilities.
What We’re Looking For
- Deep expertise building large-scale data analytics pipelines with Apache Spark.
- Comfortable using Python and AWS technologies for data engineering and ML.
- Strong communication skills to interact with technical counterparts across customer accounts to coordinate data integrations.
Nice to Have
- Experience in the healthcare and life sciences domain.
- Familiarity with ML and statistical modeling methodologies and experimental design.
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