DevOps Engineer

Contract Duration: 12+ Months with possible extensions and/or Conversation to Fulltime Required Skills & Experience• Linux / UNIX — very importanto Strong systems understanding, not simply deploying applicationso Troubleshooting in Linux environmentso Understanding how infrastructure/components interact• Dev Ops / systems engineering — primary targeto Building environments and toolingo Automationo Diagnosing complex technical problems• Python / developmento Python is highly relevant, although they did not call it an absolute requirement.o PostgreSQL was specifically mentioned as representative of their relatively straightforward techstack.• Build tooling quicklyo Look for candidates who have actually developed internal tools, automation, utilities, scripts, platforms, or solutions.o They want someone comfortable in a fast-moving environment who can get somethingfunctional and move on rather than endlessly perfecting it.• Strong troubleshooting mentalityo Can isolate whether an issue comes from their own code/design versus an external system.o Understands how to approach a new technical problem methodically.o Good fundamental CS/system intuition, including concepts such as time/space complexity andscale.• Solution architecture / requirementso Can speak directly with people, understand the actual problem, gather requirements anddetermine the simplest appropriate solution.o Shouldn't immediately assume a new technology or AI is the answer.• Large-enterprise navigationo Can still move quickly despite approvals, processes and red tape.o Strong communication and knows how to adjust communication depending on the audience.

What You Will Be Doing: Work closely with AI researchers and developers to gather project requirements and align on objectives. A strong, experienced Dev Ops / Systems / Python Engineer who understands infrastructure, troubleshooting, and how to build practical tooling quickly. AI expertise is not required. They want someone technically mature who can learn/use AI as part of the environment without treating every problem as an AI problem. Conduct in-depth research to find and curate relevant data sources to support prompt development. Keep abreast of the latest trends and developments in NLP and AI technologies.