Nuuly Sr. DevOps Engineer @ URBN Outfitters, Inc. - Philadelphia, PA 19112
a month ago
Nuuly Sr. DevOps Engineer
URBN Outfitters, Inc. - Philadelphia, PA 19112
Senior DevOps Engineer
What is Nuuly:
Urban Outfitters, Inc. (NASDAQ: URBN, www.urbn.com) is a Fortune 1000 company with $3.4 Billion in revenues offering lifestyle merchandise to highly defined customer niches through brands including Urban Outfitters, Anthropologie, Free People, Terrain, BHLDN, and Nuuly. Nuuly is the newest brand under the URBN umbrella, focusing on circular fashion and offering a subscription rental experience for women’s apparel. At Nuuly, we pride ourselves on a relaxed office culture, including flexible work from home, and campus perks, including a gym. When in the office, our employees enjoy our beautiful, dog-friendly workplace at the Philadelphia Navy Yard.
What is Nuuly Data Science:
The Data Science team is responsible for the Data Science, Machine Learning, and Analytics functions at Nuuly. Our expertise spans project management, machine learning, software engineering, data engineering, and analytics. Unlike most Data Science organizations, our team is a full-service Machine Learning solution provider, taking projects from ideation and requirements through deployment of production services. We work closely with the Engineering organization on a very wide range of projects including an in-house on-site personalization system, a dynamic pricing capability, and a warehouse optimization system for our fulfilment center in Bristol, PA.
What is the DevOps Engineer role:
Nuuly Engineering has built a state-of-the-art streaming data platform which provides ML systems unfettered access to data in real-time. As a DevOps Engineer, you will work with a team of ML engineers to take ML models into production and build deployment pipelines, including:
Experimentation: Perform exploratory proof-of-concept studies to evaluate potential deployment architectures and to evaluate new technologies.
- Design: Work with the team to design architectures for offline training and real-time deployment of machine learning models that meet feature requirements. Requirements that often drive architectural decisions include ML model format and training language, inference latency, training/inference cost, retraining frequency, and many others.
- Implementation: Work with the team to implement and maintain the ML architecture, including data pipelines and applications that enable training and inference of ML models in production.
- Deployment: Work with the team to implement and maintain automated monitoring of deployed models to assess their performance, uptime, etc.
- Strong coding skills and software development experience. Proficiency in Python is required
- Familiarity with machine learning approaches and terminology. Understanding of the practical aspects of ML, e.g. train/dev/test sets, precision and recall, overfitting, hyperparameter tuning, etc.
Experience with any of the following is a plus, but not required: They can be learned on the job:
- Experience deploying machine learning models in production
- Streaming data tools (Kafka, Kinesis, Pub/Sub, etc.)
- Datastores (relational databases, wide column stores, document stores, etc.)
- Distributed computing systems (Hadoop, Spark, etc.)
- ML tools (TensorFlow, pyTorch, scikit learn, Jupyter Notebooks, etc.)
- Data or ML orchestration frameworks (Kubeflow, MLflow, Airflow, etc.)
- Cloud platforms (Google Cloud Platform, Amazon Web Services, Microsoft Azure, etc.)
- Cloud infrastructure (Virtual Machines, Cloud Storage, IAM, etc.)
The above information has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to this job.
URBN Outfitters, Inc.
We create unique retail experiences with an eye toward creativity and a singular focus on pleasing our customer. Handle code deployments in all environments.