United Airlines Senior Developer - Machine Learning Applications in Chicago, Illinois
We have a wide variety of career opportunities around the world — come find yours.
The United IT team designs, develops and maintains massively scaling technology solutions that are brought to life with innovative architectures, data analytics and digital solutions.
Job overview and responsibilities
Support the United data engineering team by developing decoupled APIs that help meet critical enterprise initiatives. The airline’s operational systems will interact with this environment via HTTP to meet real-time classification, clustering, and scoring requirements. Drive development, deployment, and life cycle monitoring of Python based REST APIs that will be used to operationalize real time machine learning (ML) applications in a production environment.
Using formalized software engineering practices, implement REST APIs using the Python Flask framework that wrap the enterprise's ML models.
Implement highly available, fault tolerant, horizontally scalable deployment patterns for the Python APIs using Docker, deploying into Kubernetes development, QA, and production environments running on Amazon Web Services (AWS).
Develop and implement application level logging standards to measure ML model performance and effectiveness, that is used to drive the ML model lifecycle process.
5-10 years of experience in full software lifecycle development using Python, including:
Developing applications using virtual Python environments, such as pipenv.
Managing a Python package manifest inside a virtual Python environment such as pipenv.
Managing a Python code base using Git / Github.
Writing and automating unit tests.
Using Python to interact with:
Serialized models, such as .pkl or ONNX.
3-5 years of experience with REST API development using the Python Flask framework.
3-5 years of experience deploying Python applications, including:
Wrapping and testing Python applications using Docker.
Using continuous integration tools such as TeamCity to automate deployment and testing.
Using artifact repositories such as Artifactory to manage project artifacts.
Bachelor’s Degree in a quantitative field such as Engineering, Statistics, Computer Science.
3-5 years of developing applications level logging that meets the following criteria:
Measuring application performance.
Logging ML predictions in a way such that they can be compared against actual outcomes to measure model effectiveness.
Aggregating logs form a Python Docker / Kubernetes environment using frameworks such as:
ELK or EFK
3-5 years of experience using AWS Elastic Container Service (ECS) and Elastic Kubernetes Service (EKS).
Experience working in a machine learning / artificial intelligence environment, and interacting with data science teams.
Must be legally authorized to work in the United States for any employer without sponsorship
Successful completion of interview required to meet job qualification
Reliable, punctual attendance is an essential function of the position
MS in a quantitative field such as Engineering, Statistics, Computer Science.
Familiar with distributed machine learning with Spark or H2O is preferred
Familiar with ML DevOps platforms, such as Algorithmia, DataRobot, or ParallelM.
Equal Opportunity Employer – Minorities/Women/Veterans/Disabled/LGBT
Division: 21 Digital Products & Analytics
Function: Information Technology
Equal Opportunity Employer – Minorities/Women/Veterans/Disabled