Publicis Media Group

Developing Publicis next gen ad campaign and analytics portal - called growth OS

Publicis Growth OS

MoD (sub contractor of Monitor IS)

Development of MoD incident reporting and learn system called : DURALS

MoD DURALS reporting web system

Johnson Controls

Developing JCI's next generation fire panel management software

Consys - Next gen fire panel management software

Full stack software engineer

Pypi

Python interface to the Google's Firebase REST APIs

Firebase

J.P. Morgan Chase & Co.

The firm was rolling out a new SSO (Single Sign On), password-less authentication system, which meant application developers needed to migrate their current authentication methods over to this new system. This obviously was no small task, and to correctly verify, authenticate and validate users using the new system was vastly different to the previous access-control tool that was in place. I saw that with countless applications needing to make this change (near 1,500 lines of code per application), it would cause issues.

So I created a Python package which is an intermediary between the application and the authentication tool. This standardised the authentication process for application teams, which only took them 5 lines of code while using my package, increasing security with above industry standard authentication and decreasing technical debt. It quickly became the firm’s strategic standard for Python authentication to use my package.

JPMC Authentication Package

JP Morgan Chase

A system for implementing a Sentiment Analyzer Tool, the system comprising: a data repository that stores and maintains sentiment data; a user interface that graphically presents metrics based on the sentiment data; an application program interface in communication with the data repository and the user interface; and a processor, coupled to the data repository, the user interface and application program interface, configured to perform the steps of: receiving, via an electronic input, real-time customer communication; extracting, via the processor, text data from the real-time customer communication; generating, via the processor, a customer sentiment score based on the text data; comparing, via the processor, the customer sentiment score to a threshold value to determine a positive sentiment, neutral sentiment or negative sentiment; and graphically representing, via the interactive user interface, the customer sentiment score as compared to sentiment data associated with a plurality of other customers wherein the interactive user interface displays overall sentiment data, sentiment data over a predetermined period of time and a combination of: emotion data, social tendencies data and language style data.

System and Method for Implementing a Client Sentiment Analysis ToolSystem and Method for Implementing a Client Sentiment Analysis Tool US DN/20190220777

Python Human Movements is a Python package which imitates human movements.

pyHM

Handlr is a Python handler to logging services, i.e. Logstash as part of the ELK stack.

Handlr

There was a business need to improve the time to market on the provisioning of new operating systems for an accelerated computing offering. At the time the system that was in place could take weeks and in some cases months to complete, with a success rate of almost zero since every order would require manual intervention.

I wrote a suite of APIs which communicated with our estate management nodes, the firm’s compute ordering tool and the firm’s build pipeline orchestration tool. This new solution has been operational since, with almost daily production releases, with new features released to our clients with our continuous innovation, improving our customer’s userexperience. The provisioning times went down from weeks/months to just hours, with it being fully automated end-to-end. Removing the need for manual intervention as a negligible number of builds require attention, and those that do are automatically notified to the team without the client even knowing there was an issue.

OS Build Automation

I had noticed that a fully automated Jenkins pipeline for Java was implemented internally, however there was no fully complete end-to-end Python solution. I was writing a suite of Python REST APIs at the time, releasing new features into production on a some-what regular basis. However, the major blocker was from lengthy wait times from other teams testing, scanning and approval of my code. I took the opportunity to write the firm’s first fully end -to-end Python SDLC pipeline, it was written in Groovy and ran on Jenkins, just like the Java one to keep the tools standardised.

It automated the build, testing, scanning and deployment of an application, and its subsequent new features into a production environment, automatically meeting the firm’s and regulatory requirements via the automated scans and associated “toll-gates”. All of which would happen automatically from the point a developer was to push their code into a SCM (Source Code Management System, for example GIT), which they would have to be doing anyway. For a firm with over 40,000 technologists, this huge time-save was a big deal. I was flown over to New York and New Jersey and gave presentations to 100s of the firm’s most senior developers. By showing them my work and implementation strategy, they too could utilise my pipeline for their applications. Saving them and the firm time, increasing value -add throughput, lowering code smell, all while increasing their applications security and improving their time to market.

Expert, freelance OOP engineers