Machine Learning Engineer
A
AbbViePharmaceutical
United StatesFull-TimeMiddle
Salary109,500 - 208,500 USD per year
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Job Details
- Experience
- 3+ years
- Required Skills
- AWSDockerPythonSQLPyTorchPandasTensorflowscikit-learnMLOpsPySpark
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 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.
- 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.
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.
- Document and communicate work progress, technical decisions, and outcomes to technical and non-technical audiences.
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