Job Description: This is NOT a traditional full stack or backend role anymore. ✅ Built end-to-end AI / agentic applications ✅ Understand what goes into an LLM, what comes out, and how to use it ✅ Can productionize AI systems (not just prototype) 2. Ability to Turn LLM Output into Action Key expectation: “Don’t just call an LLM — do something meaningful with the output” Candidates should demonstrate: Structured output handling Workflow orchestration Turning AI responses into usable application features 3. Productionization > Prototyping This is where candidates are currently falling short. Ross is heavily focused on: How AI apps are moved into production environments Understanding of: CI/CD in enterprise environments Deployment workflows Reliability & operational considerations ✅ They don’t have to build CI/CD pipelines themselves ✅ But must understand how production systems work at a high level 4. Deep Understanding of Agentic Systems Candidates must be able to confidently discuss: Memory management in agentic applications How agents: Maintain context Store/retrieve information Execute multi-step workflows