What are the responsibilities and job description for the Software Engineer II- AWS / Machine Learning position at JPMorgan Chase?
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, AWS / Machine Learning at JPMorgan Chase within the Corporate Sector-Consumer and Community Banking (CCB) Risk, you are part of 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 Risk Quantitative Development (RQD) Model Implementation team helps modelers implement their models into production. Our activities include perform hands-on coding primarily using spark and python to implement complex regulatory or risk models in production. This team is also responsible for enabling ML capabilities on AWS.
Job responsibilities
- Executes standard software solutions, design, development, and technical troubleshooting
- Contributes in a development and ops role as a hands-on technical member of the ML engineering team and works closely with Modeling and other Technology teams within the firm
- Supports the model development activities team, manages model development infrastructures and provisions ML software, libraries, packages and tools required for ML model development
- Writes secure and high-quality code using the syntax of at least one programming language with limited guidance
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
- Designs and sets up Machine Learning platform infrastructure to develop and train machine learning models for Risk and Fraud
- Sets up ML tools on AWS infrastructure through automated pipelines and using Terraform (Infrastrucure as Code)
- Designs and develops tools to Support machine learning platforms for critical CCB Risk and Fraud models
- Enables distributed machine learning techniques on Xgboost, TensorFlow, Scikit learning using EMR RAPIDS, Spark, Dask and Sagemaker
- Applies knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automation
- Learns and applies system processes, methodologies, and skills for the development of secure, stable code and systems
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 2 years applied experience
- Hands-on practical experience in system design, application development, testing, and operational stability
- Proficient in coding in one or more languages including Python or Java
- Experience with Machine Learning model development
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
- Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- Hands on experience with AWS-EC2, S3, EMR and Sagemaker to migrate machine learning models to AWS
- Should have good understanding and knowledge in planning and managing AWS Cloud infrastructure and enabling necessary platform security by using best in class Cloud Security Solutions
- Hands on experience in application development using EC2, S3, EMR, Lamdba
- Experience provisioning AWS resources into multiple cloud accounts through automated pipelines using Terraform
- Experience with AWS platform tools like Cloudwatch, Datadog for monitoring utilization and for setting up thresholds and performance alerts
Preferred qualifications, capabilities, and skills
- Familiarity with modern front-end technologies
- Hands on experience in migrating Spark applications from Bigdata Hadoop platforms to AWS Cloud
- Experience in the design, implementation, and support of advanced machine learning tools such as Anaconda packages, XGboost, Scipy, numPy, Pandas, and Tensorflow to support model development
