Day-to-day work
- Design API flows and webhook handlers around model calls.
- Implement RAG or tool-calling patterns with fallbacks for when the model is wrong, slow, or down.
- Build evaluation harnesses so prompt and model changes are tested rather than vibes-checked.
- Pair with product on cost/latency tradeoffs.
- Write runbooks and participate in incident reviews over Slack or Linear.
Employed vs marketplace: the fork in remote AI engineering
Look at real listings and a pattern jumps out: much of the senior remote work routes through marketplaces rather than direct employment. Of the engineering roles on our jobs page (fetched July 2026 from Remotive), A.Team places contract AI engineers at $120–170/hr on client projects. Lemon.io matches engineers to startups at negotiated rates — neither is a conventional job.
The tradeoff is real. Contract rates run well above most salaries, but you carry your own benefits, and your pipeline can gap between projects. Both networks also screen hard on entry. Direct-employment remote roles exist and skew toward companies whose product is itself AI-adjacent; expect them to test production judgment, not just model familiarity.
Skills to demonstrate remotely
Public repos or short recorded demos beat timezone-overlap alone. The differentiator in 2026 is not "has used an LLM API", because everyone has. It is evidence you have run one in production: an eval suite that gates deploys, idempotent background jobs, structured logging that let you debug a bad model response after the fact, or a story about a cost or latency cliff and what you did about it. One deployed system with honest documentation of its failure modes outweighs five demo notebooks.
Interview prep
Expect system design for a small LLM feature, such as a summarization queue or a support-ticket router. Expect questions about data privacy and what you would refuse to log, plus a take-home with a 48–72 hour window. Reviewers of take-homes consistently reward documented tradeoffs. A README explaining what you would do differently with more time reads as senior; a maximal feature list does not.