CSS

Upon joining, I came up to speed quickly on the stack of the early version of Pull Workbench, which was very buggy but demonstrated the initial ideas and had a solid set of the latest technologies and patterns established in the codebase, providing for a solid starting point.

I was entrusted to aid our CTO in hiring several additional employees, and so I joined and conducted interviews for the first several months while working with the existing team AI + Full Stack to deliver features and solidify the system, with the aim of keeping it fully working with each merge, after playing a little catch-up to fix the early bugs that worried our business partners, giving them confidence that our team could deliver.

From there, I developed full stack features solo or by pairing with team members, and ultimately led a squad of 5 team members alongside a second squad that together comprised our engineering team.

Much of my time went into authoring complex analytics sql queries using the impressive Kysely library, a fluent, typesafe query builder that we used for our postgres and redshift databases. Given the nature of the product, we needed to make decisions on which queries could be run in real time vs. which queries and subqueries would need to be computed offline as part of a network of airflow dags.

On the ML Ops side I advocated for traceability and reproducibility / determinism of all models and artifacts, and integrated with systems that implemented that, such as Airflow to coordinate DAGs of ML training jobs and Sagemaker's metadata API, which we controlled via model lifecycle automations that produced and stored models, artifacts and metadata that were in turn consumed at runtime or in batch by our analytics stack

On the frontend, I helped us deliver an initial version of the Pattern Editor, a UI and set of APIs that users could use to put together their own patterns of interest, such as looking for certain anomalous ranges of quantities that themselves may be derived from other user-defined patterns. This entailed not only a UI that was DAG-aware but also a layer that converted the json representation of these patterns from the frontend into typesafe kyesely queries to be executed against redshift.

Interview Builder is where customers would go to define the values they wanted to find in their ideal candidate, and map those to attributes and ultimately interview questions that the intertru ai was pre-trained to assess.

My role was to work closely with the CTO to understand what was proven out on the ML side, so that we could deduce a UI that intuitively would extract the necessary inputs from the customer, while providing them with predefined templates as starting points to ease them into the process.

The application was built on react, typescript, graphql (backed by dynamodb) and amazon amplify, and I built it very quickly with simple backends so that we could iterate on the frontend, to get the experience right before investing significant time and effort into an ideal backend. This approach made iterations faster and produced less collateral damage / throwaway code as we refined the user experience.

We then added instrumentation so that we could measure the use of the feature, any bugs that might turn up, and its performance, before releasing it to production, where it was initially used internally to surface any shortcomings before customers were exposed to it.

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

I ran Appen's ML Platform, which was used by FAANG and many other startups and enterprises to automate and scale their ML practices, including running both supervised and unsupervised workloads, as well as their global annotation workforce which enabled customers to leverage our crowdsourced professionals to elastically obtain labelling and quality checking services for text, voice, image, video and LIDAR annotation, training and validation use cases.

I reported to the CTO and directed multiple full stack teams each with their own tech leads and range of engineering skills to do both regular maintenance and product enhancements using technologies like Sagemaker, React, K8s (Kubernetes), Spark, Kafka, Airflow, Spring(Boot), Ruby, Python, Java, Typescript and SQL.

Maintenance included regular updates to infrastructure, bug fixes, and performance optimizations across the platform. We migrated more and more services to K8s (Kubernetes) and Ambassador as our API gateway, where we could consolidate cross-cutting logic like auth and versioning.

Enhancements included changes to simplify the UX, kill redundant or unused features, add measurement to inform our choices, and larger efforts like Enterprise OAuth.

There were 4 different websites in different technologies, acquired from different companies, and some APIs, that all needed to be unified in terms of sign up, sign in, and sign out, given their existing state of each having separate user stores, including 3rd party vendor users who logged in with vendors and then authed to us with a hidden token.

It was a stalled project, so I started with missing requirements, incomplete designs and misleading progress indicators and focused other leaders and teams on delivery thru tested working software, focusing on tested user stories and on-the-ground learnings as units of progress, instead of large, outdated PRDs waterfall style.

Contributed directly in React / Typescript, Nodejs / express, Ruby on Rails and custom gems, OAuth configuration, Java Spring with runtime loaded SPI implementations from across separate applications domains.

There was a complex architecture at play and teams that did not know each other and weren't working as a single unit, so the landscape was difficult and rife with demoralized team members.

Although my team was to play but one part in many on the project, I realized quickly that there was no single leader or coherent plan, and so there was lots of blame game and treading water.

With permission from our VP of Engineering, I took charge of the teams and worked with product to firm up requirements, and replace the initially conceived solution architecture, which would not have worked and was created in a bit of a vacuum, into one that would actually work, by digging in and running all the services and web apps myself and understanding the multiple data stores and existing auth mechanisms including auth via 3rd party vendors to some parts of the system.

I delivered the project within 5 months and for my efforts was rewarded not long after with a promotion.

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.

Progressive had two sites for Direct Auto Quoting - one used by customers at home and the other used by our call-center reps. Given the complex state-by-state variance in the insurance laws, this made for a huge maintenance cost, and doing it twice in two codebases didn't make sense.

The premise was that a single web 2.0 Direct Quoting Application could replace these while also yielding a much more modern and customer-delighting application.

As the team's Actionscript expert, I joined and quickly helped out delivering feature after feature, and I loved the XP discipline and grew to appreciate TDD especially after the site started hitting performance problems that warranted significant refactors, which would have been much riskier without test coverage!

Our pilot included 14 of the 50 states, and was a complete success. However, the amount of clientside rules and assets started slowing the app down, and this was when I was asked to replace the REF framework that preceded my joining, with something much faster and more developer friendly. This led to my promotion to lead engineer, where I begun work on REF 2.0 (UI Framework)

Cancels was Progressive's first attempt at enabling policy cancellation without making a call to the call center and talking to a human.

The business wanted to find out key information that would aid them in avoiding cancellations in the future, as well as to provide off-ramps to cancellation that users might find enticing.

I worked with the business to understand the requirements, which allowed the user to navigate back and forth, answering questions that implicated which other questions were relevant, including questions in the future flows that the user may have already navigated through.

The UI was accordion-like, with eased animations, lighting, visual progress indicators and complex business logic that varied due to state-level regulation variance in the insurance industry.

I completed the application and it was delivered to production where we got a higher rate of cancellation data for scientists than we were capturing in our call centers. This helped us better understand the motivators for cancellation.

The success on this project is what got me brought in to help lead a much more significant effort - a brand new auto insurance policy quoting system for all 50 states - as a full-time employee

Retirement Readiness App - Architected and built, along with more junior colleagues and product owner, a webapp whereby the user, after providing some basic demographic information, would be interviewed by one of ~10 pre-recorded actors in natural language, in order to produce a "Retirement Readiness Score", charts, and cross-sell product offerings.

Capabilities I provided included:

• A successful, performant application
• Caused the emergence of clear formulae and determinism from vague business goals
• Video captioning and screen reader for Accessbility
• Leadership of Technical + Project Team
• Clean, Organized, Testable Code and Solution Design

Spontaneous Leadership - Funny story, but our actors arrived at the studio on film day, and the director was unable to come. When it became obvious that nobody was particularly experienced or prepared to take on that role, I offered to do it, and so I collaborated with actors and the team to ensure we captured what we needed against the green screen, with proper lighting, tone and personalities the clients had asked us to obtain. I am ready to step in and lead or solve problems outside my expertise or experience, if it is what the team needs at the time.

We went through several iterations of the website, but our goals were SEO optimization and systematizing the internal booking process with salesforce while exposing certain information about entertainers, acts and availability through the website, and keeping those in sync with entertainer calendars.

With the help of some interns, I designed and built the website from the ground up, and hosted it at Cleveweb.com, the software solutions company I had formed a few years earlier.

montgranite.com - supplier of natural stone website. After assessing their products, we categorized them according to stone type, colors, texture and brand, then I created a MySQL database and an ORM to read/write PHP objects and their relationships from the database and present it as a front-end, which I designed to resemble a piece of marble.

I then hosted the company's website and email for a number of years afterward and actively updated the site on retainer.

I built a plain web and interactive flash based website for mpercent.com, an art gallery, backed by a php web server and mysql database, containing various works of art, artists and events that were being presented and searchable to end users

Working with executives, I designed, built, hosted, continuously updated, and reported traffic analysis reports for Northcoast Technologies. I designed and built an animated advertisement explaining their product.

Rick Smith Jr. was one of my best customers, we did 3 different versions of the website over an 8 year span. They included advanced animations and rich video experiences as well as booking forms and SEO optimizations.

Thetaylorcompanies.com had a searchable cataolgue of many pieces of furniture, each with multiple image angles, specs, descriptions, brands, pricing, and delivery schedules. After understanding their needs, I put together a MySQL database, PHP web server with a custom ORM, and an rich frontend with an animated intro page.