Machine Learning Engineer

Allergan Aesthetics, an AbbVie company, develops and markets aesthetics brands and products. The Machine Learning Engineer will build, deploy, monitor, and refine machine learning systems and pipelines, while collaborating with technical and business partners to deliver production AI solutions.

Responsibilities:

• Own small to medium components of machine learning systems from technical designthrough implementation and delivery
• Translate technical requirements into high-quality, maintainable code and deliver workstreamsaccording to plan
• Build and maintain data pipelines and feature engineering workflows to support machinelearning and AI solutions
• Design, train, evaluate, and refine machine learning models with minimal supervision, applyingsound 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 modelperformance, data drift, anomalies, and retraining triggers
• Collaborate with Data Engineers, Software Engineers, Data Scientists, Product partners, andbusiness stakeholders to deliver project objectives
• Understand system design, data models, and technical artifacts well enough to contribute toimplementation decisions and tradeoffs
• Follow governance, documentation, coding, and source control standards consistently
• Demonstrate flexibility and proactively support teammates with day-to-day responsibilities asneeded
• Clearly document and communicate work progress, technical decisions, and outcomes totechnical and non-technical audiences

Requirements:

• 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 scienceprinciples
• 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 performancemonitoring, data drift detection
• Working knowledge of SQL and relational data structures
• Ability to design, train, and evaluate machine learning models using standard best practicessuch 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
• 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