You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you.
As a Software Engineer II, Machine Learning at JPMorgan Chase within Consumer and Community Banking Risk Corporate Technology, within an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role. This is a development and operations role and the candidate will be a hands-on technical member of the ML engineering team and will work closely with the Modeling team and other technology team members within the firm.
Job responsibilities
Creates secure and high-quality code using the syntax of Python or Java language with limited guidanceDesigns, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implicationsExecutes knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automationExecutes technical troubleshooting to break down solutions and solve technical problems of basic complexityGathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application developmentCreates object-oriented design, test framework for UI and API, agile development methodology, an understanding of and some exposure to all aspects of the project life cycleDesigns and sets up Machine Learning platform infrastructure to develop and train machine learning models for Risk and FraudOperates ML tools on AWS infrastructure through automated pipeline and using Terraform (Infrastructure as Code)Designs and develops tools to Support machine learning platform for critical CCB Risk and Fraud modelsEnables distributed machine learning techniques on Xgboost, TensorFlow, Scikit learning using EMR RAPIDS, Spark, Dask and SagemakerDevelops automation scripts using Python and Unix Shell Scripting
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 2+ years applied experienceExperience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languagesExposure to agile methodologies such as CI/CD, Application Resiliency, and SecurityFull understanding of SDLC, QA and Defect LifecycleStrong experience in developing test automation scripts with object-oriented programming languages such as Java using Eclipse/IntelliJ IDEHands-on experience with Java, Python and SeleniumUpgrade/create new reusable tests framework for API and UI using Cucumber, Selenium, Java as per functional requirements.Exposure to middleware technologies and Messaging queues and databaseExperience using continuous integration tools such as Jenkins.Experience in UI, Database and API testing.Implement end-to-end automated tests to run across multiple applications.
Preferred qualifications, capabilities, and skills
Experience with cloud technologiesSound SQL and DB knowledge (Oracle, Cassandra) Experience in developing In-Sprint Test Automation scripts is an added advantage.Experience in latest cucumber-based frameworks is an added advantage.