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Appen AI
Formerly Figure Eight2022 - 2023
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.
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.
Key Results
- Reduced deployment lead time by 75% enabling product teams to self-serve infrastructure
- Ran ML platform to support 100K+ annotation jobs daily across FAANG clients
- Decreased inter-team ticket volume by ~60% through developer empowerment