Hiring a remote AI developer in India fails less often on coding skill and more often on onboarding. Missing API keys, unclear data rules, no acceptance criteria, and silent Slack channels turn a two-week sprint into a six-week fog.
Use this checklist before day one so the first AI milestone is measurable.
1. Access and environments
- Separate staging credentials for OpenAI/Claude (never production keys in local laptops if avoidable).
- Repo access with least privilege; document branch and PR rules.
- Staging app URL, database snapshot policy, and seed data for RAG tests.
- Issue tracker project with a single owner for priorities.
- Secret store process (1Password, Doppler, Laravel env, etc.).
2. Data and privacy policy in writing
State what can be sent to model providers. List forbidden fields (passwords, full payment data, health identifiers if regulated). Decide logging retention. If customer docs will be embedded, clarify retention and deletion.
Ambiguity here creates legal and trust risk later.
3. First milestone definition
Pick one thin slice, not a platform rewrite. Examples:
- RAG over a fixed 50-document corpus with citations and a "I don't know" path.
- Support chatbot draft replies with human publish gate.
- Structured extraction from tickets into JSON fields.
- Streaming chat UI against a backend endpoint with abort and retry.
Write acceptance criteria: latency budget, accuracy sample set, cost ceiling per 100 requests, and who signs off.
4. Evaluation set before coding
Prepare 20 to 50 real questions or cases. Include edge cases and out-of-scope prompts. Without a golden set, "it looks good" becomes the only metric.
5. Communication rhythm
- Async daily update: done, blockers, next.
- Overlap hours for demos and decisions.
- Weekly cost and quality review once the feature is live in staging.
- Decision log for prompt and model changes.
6. Week-one agenda
- Architecture sketch: where LLM calls live, queues, stores.
- Threat and privacy review for tools and logs.
- Implement the thin slice behind a feature flag.
- Run the golden set and share results honestly.
- Plan the human review path if outputs are customer-facing.
7. Red flags during onboarding
- Requests production customer data on day one without need.
- Cannot explain token cost drivers.
- Pushes agents everywhere when a single prompt would do.
- No written assumptions after the kickoff call.
Summary
Remote AI success is an ops problem as much as a model problem. Clear access, data rules, a thin milestone, and a golden set beat vague "build AI" briefs.
Ready to engage? Hire AI Developer, read AI MVP Development, or start a conversation.
Prakash Tank
Full-Stack Architect & Tech Enthusiast. Passionate about building scalable applications and sharing knowledge with the community.
