Team Building

The candidate summary page summarized a candidate's performance during multiple interview stages by presenting radar charts showing degree of fit against the values and attributes being evaluated for their position, as defined in the Interview Builder.

I built the frontend in React and Typescript, and integrated with the backend, which I partially built, which leveraged RAG and ran several Machine Learning models to produce scores and explainable AI. For example, models to break down interview transcripts into quotable fragments, evaluate relevance against configured company values, and call chatGPT APIs to obtain summaries and scores related to that content

Instead of splitting devops and infrastructure and tests completely separate from development teams, I moved the needle so that product development teams could own more of their own infrastructure and tests, creating less back-and-forth and empowering teams to deliver.

We used Devspace, which meant any dev or team could stand up a reproducible, isolated stack with multiple services and frontends running, in the cloud, as well as modify the definitions of the infrastructure and code themselves, directly, without permission or external team tickets.

This enabled product engineers to do more experimentation and testing thru declarative infrastructure and configuration management while still protecting our production environments, unlocking their shackles and potential as the experts in the software.

At the same time I worked to reduce the outsized role our amazing DevOps team was playing in the day to day management as well as enhancement of environments, which unfairly impeded expert developers by introducing red tape and inter-team processes that didn't add value.

My PM and the business wanted to illustrate to other teams that a fast-paced, fail-fast approach where we released daily (as opposed to 1-3 times per year) would serve us much better in that we could learn quickly, iterate and pivot, without huge costly investments into products that did not meet expectations or deadlines.

Before hiring my team, I set up a CICD pipeline and basic framework of a site that could sustain a heavy and intense crawl from google.

New hires all released to production on their first day of work - a principle I had brought to the table, that it should be so automated and simple that someone could set up and deploy a small feature within their first few hours of working at Sourceability.

Our parts and datasheets website, which also incorporated proprietary availability and quality scores, was used - within 3 months of inception - to successfully sell a 3 year Analytics API contract to an international multibillion dollar company, as well as driving organic traffic and learning how to scale to sustain google crawls of the hundreds of thousands of electronic component parts in our inventory while scaling down outside of the crawl / high-traffic moments.

• Full Stack - React, NodeJS, Typescript, Kubernetes, Gitlab
• Functional Reactive Programming - RxJS, highlandjs
• Daily Production Deploys - Canary Deployment w/ K8s
• Constant Collaboration - No “throwing over the wall”
• CI/CD Automation Pipeline - Every user story gets an instant shareable environment
• Coaching / Mentoring / Leading diverse team

A working prototype and specs for how heartpoints would be rewarded and exchanged and linked to "Proof of Good" that could be validated off-chain (since proof of good in this case may for example, be video evidence or other data that is too large to fit onto the chain), using a strategy of hashing the proof and storing the hash and URL of the proof's off-chain content.

A portal bringing together version control, automated test definitions and statuses, quality metrics, jira tickets, CICD jobs, and supportinginfrastructure definitions and status into a single place to aid in release management and devops practices.

Behind the scenes, pipelines made with K8s (Kubernetes), Mesos, Github and Jenkins automatically provisioned environments, deployed our software and ran extensive tests on it, including complex multi-cloud platform scale tests across Google Cloud and AWS as well as on prem with bare metal and Open Stack

I initiated and led "Scala Dojo" ,partnering with QA / Devs interested in adoption of Functional Programming, leading to certifications from Coursera and improved team morale and interest.

That broadened and continued on as "DevOps Dojo", which was a recurring collaboration initiative that produced theDevOps Portal + CICD by engaging across teams and disciplines to ascertain true priority pain points and solutions that scaled across multiple teams, so that we could address those via CI/CD and our DevOps practices.

Led a series of workshops to teach Scala to various engineers at the company, including concepts like groups, monads, folds, lifts, pattern matching, case classes and higher kinded types.

Temporal Sequencing - Architected, implemented and tested an enhancement to our proprietary Explorys MDL (measure definition language), which gave authors who use that language the ability to express dynamic temporal conditions as part of the predicate calculus that constitutes the measure being defined in a given MDL instance.

The solution runs in Hadoop as a mapper, where the MDL is parsed and a corresponding object graph is constructed, and then control is passed to the encapsulating object of that graph, resulting in a determination about the level of adherence of the given patient to the measure defined by the MDL which is then emitted for further processing.

The challenge here was authoring the solution without knowing the history or complexity of the existing system. The challenge was overcome by applying SOLID principles and TDD principles to express assumptions about the existing external system as interfaces, getting the solution to work in a test sandbox as a standalone application, and then once that was done, writing adapters between the actual system and the interfaces that were formerly mocked out for testing. The team which owned the system seemed impressed by and curious about the solution and the new patterns / tests involved.

By taking what had been considered a relatively tough problem, and, as an engineer with no history in the measure engine, applying an approach that allowed a fully tested solution to ultimately be plugged into the larger system very cleanly, as an implementation of a clear set of interfaces along the border of that system, I exposed the team to new patterns and solution design approaches that I hoped the team might take forward and build on as the system continued to evolve.

Example Use Case: Doctor wants measure of % of his/her patients having Hospitalization with outcome class X, where either 1 week later, Rehospitalization occurs or within 2 weeks, unexpected Office Appointment occurs. Doctor expresses using natural language oriented temporal sequencing expressions within model definition language.

Consulted and advised Enterprise Steering Committee on adoption of Agile processes within the broader SDLC, which was being standardized away from waterfall.

Conceived of, designed, prototyped, developed, tested and rolled out various solutions for improved modelling and automation of several development-related business processes, spanning the software development lifecycle from requirements to system retirement. Created bidirectional traceability between requirements, code, test, and defect data and metadata from various disparate systems and technologies including Quality Center, Sharepoint, Visual Studio and TFS using REST, JavaScript, .NET and WCF services and an Enterprise Service Bus model.