
Build AI solutions and integrate into development life cycle
Evaluate new AI solutions appearing on the market
introduce tooling for administration such as performance or AI credit consumption monitoring
Defining and tracking operational excellence standards
The charter is wide, the opportunities for organizational impact are boundless, and we have your back to try new things and see if they stick and do, in fact, make everyone more efficient.
The Role
We are looking for a highly skilled and experienced Software Engineer to contribute as part of this new team to help offer a great service and experience to their fellow colleagues in the other feature engineering teams. Here you are expected to have a real impact on the engineering platform, helping evolve our technology stack and build reusable components that help the other backend teams.
This role will pave the way to improving the developer experience by creating internal frameworks, SDKs, pipelines, and platforms that accelerate the entire AI development lifecycle.
If a wide leeway to improve developer productivity and production reliability sounds exciting to you, come talk to us!
Directly contributing to the vision, strategy and roadmap for the Intelligent Tooling and Enablement Team
Driving excellence by providing quality code, performance, maintainability
Identifying common themes in upcoming projects and building common frameworks to help teams build them faster
Working with engineering teams to find out where we need to invest in more tooling
Triaging feedback and bugs from users
Engaging with the open-source community
Building reliable core service libraries so feature teams can focus on business logic
Experience maintaining shared libraries, especially open-source libraries
Experience with container-based workflows and services, especially Kubernetes
Experience building production monitoring to track service and website health
Experience with Golang (used extensively by the Dev Tooling team)
Familiarity with Typescript, Python, and Ruby (used elsewhere in the company)
Hands-on experience with AI coding assistants, AI-based testing tools, and automation frameworks used in modern software engineering.
Familiarity with modern AI technologies such as LLMs, prompt engineering, RAG architectures, model evaluation, and AI-assisted development tools.
Strong knowledge of development workflows including planning, coding, testing, CI/CD, and deployment.
Experience with modern cloud environments and AI infrastructure (AWS, Azure, or GCP).
Understanding of how to connect AI systems with enterprise data sources (documentation systems, code repositories, ticketing systems).
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Between 10 - 50 employees
2014