Allergan Aesthetics, an AbbVie company is hiring a Machine Learning Engineer

Responsibilities Own small to medium components of machine learning systems from technical design through implementation and delivery Translate technical requirements into high-quality, maintainable code and deliver workstreams according to plan Build and maintain data pipelines and feature engineering workflows to support machine learning and AI solutions Design, train, evaluate, and refine machine learning models with minimal supervision, applying sound statistical and engineering practices Implement ML solutions that can be deployed into production environments as microservices, APIs, batch jobs, or streaming components Support production monitoring efforts by helping define and implement metrics for model performance, data drift, anomalies, and retraining triggers Collaborate with Data Engineers, Software Engineers, Data Scientists, Product partners, and business stakeholders to deliver project objectives Understand system design, data models, and technical artifacts well enough to contribute to implementation decisions and tradeoffs Follow governance, documentation, coding, and source control standards consistently Demonstrate flexibility and proactively support teammates with day-to-day responsibilities as needed Clearly document and communicate work progress, technical decisions, and outcomes to technical and non-technical audiences Required Experience & Skills Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field 3+ years of practical experience building, evaluating, scaling, and deploying machine learning pipelines with Python Strong programming skills in Python and solid understanding of core computer science principles Experience with data manipulation frameworks such as Pandas and PySpark Experience with machine learning libraries such as scikit-learn, HuggingFace, TensorFlow/Keras, PyTorch, or MLlib Experience with MLOps practices such as automated model deployment, model performance monitoring, data drift detection Working knowledge of SQL and relational data structures Ability to design, train, and evaluate machine learning models using standard best practices such as model selection, validation, bias/variance tradeoffs, and performance assessment Familiarity with batch and streaming data pipeline concepts such as ETL, ELT, and stream processing Experience working with cloud environments, preferably AWS Familiarity with technologies such as APIs, microservices, Docker, and Kubernetes Strong interpersonal, verbal, and written communication skills Ability to work effectively in a remote environment using collaboration tools Preferred Experience & Skills Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science Experience with managing and architecting solutions on AWS Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, Data Cataloging tools, Data Observability tools and Data Governance tools