#738347BR
tion Full Stack Developer working in IBM Cloud needs to have a passion for technology and thrive in team environment, certain to leverage an outstanding technical community, advanced and innovative technologies to build own career growth.
Responsibilities in this role encompass identification and prototyping security architectures and technical solutions in order to support, enable and improve all the Security Functions (Identify, Protect, Detect, Respond, Recover, Compliance) across the entire IBM Cloud Platform through innovative solutions.
We are looking for someone who collaboratively contributes to design discussions and decisions, performs code and design reviews with other team members, produces documentation for the work done, and iterates to pursue solution improvements. A Fraud Prevention Software Developer also supports security tools' adopters in order to investigate, troubleshoot and address issues.
The Fraud Prevention Software Developer can work in an environment where agile is adopted as development process across different geos and cross organization.
Required Technical and Professional Expertise
Extended experience on agile methodology
Bachelor's in technical discipline, Computer Science (or relevant)
5+ years experience in software development
Proven knowledge and practice on data science, AI, Machine Learning
Experience on updating and managing relational databases (PostgeSQL)
Knowledge and practice on Kubernetes, CI/CD pipeline tools (Travis, Jenkins, Tekton), git/github
Experience on NodeJS, React and at least one among Python, GO, Ruby, Java, Javascript
Knowledge of Cyber Security principles
1+ years of experience contributing to the system design or architecture (architecture, design patterns, reliability and scaling) of new and current systems.
Strong debugging, troubleshooting, and problem solving skills.
Ability to take a project from requirements analysis through to launch and operation of the system in production.
Implementing and maintaining statistical models
Building and evaluating predictive models using statistical and machine learning techniques
Design batch and stream processing pipelines
Experience with tools for big data, ETL and data warehouses
Preferred Technical and Professional Expertise