I am looking for an n8n engineer / AI automation engineer who speaks Indonesian to audit and fix production workflows.
Context:
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System runs on n8n
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Integrations: Telegram, WhatsApp/Messenger flow, AI agent, Google Drive/Pinterest, webhook, reporting
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Issues: workflows are not yet stable, need end-to-end audit, error handling, logging, fallback, and fixes without breaking the running system
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Not a job to create demo workflows from scratch
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Must be able to read complex workflows, trace execution, fix bugs, create test cases, and provide evidence before claiming completion
Requirements:
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Experienced with n8n production workflows
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Understand webhooks, APIs, credential security, error handling, retry, logging
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Understand AI agent / LLM workflows
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Speak Indonesian
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Willing to work via staging/sandbox, not directly tinkering with production
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Able to provide documentation of changes and test evidence
Send your n8n portfolio, examples of cases you have handled, and your estimated approach for conducting the audit.
2 Likes
Halo ady_sihombing,
Saya tertarik dengan project audit n8n production workflow Anda. Latar belakang saya:
Pengalaman:
- n8n production workflow dengan integrasi Telegram, WhatsApp, API, webhook, AI agent/LLM
- Error handling, retry logic, logging, credential security
- Audit sistem kompleks yang sudah berjalan tanpa mengganggu production
Metodologi audit yang saya tawarkan:
- Duplikasi workflow ke staging/sandbox
- Tracing execution path end-to-end
- Identifikasi bottleneck, gap error handling, dan single point of failure
- Implementasi: retry with exponential backoff, fallback mechanism, structured logging, alert notification
- Test case untuk setiap node kritis
- Dokumentasi lengkap + bukti test sebelum deploy ke production
Saya menawarkan fixed price $750 untuk full audit + fix. Estimasi 3-7 hari tergantung kompleksitas workflow.
Jika tertarik, silakan kirim akses ke staging/sandbox. Saya bisa mulai analisis dalam 1x24 jam.
Terima kasih.
Olá, Ady! Tudo bem?
Tenho experiência com workflows n8n em produção, incluindo integrações com WhatsApp, AI agents/LLMs, Google Drive e webhooks, exatamente o tipo de stack que você descreveu. Falo português nativamente e já trabalhei em projetos que exigiam correção de bugs sem quebrar sistemas já em funcionamento.
PortfĂłlio: https://www.jeffersoncampina.tech/
Exemplos de casos relevantes:
- Desenvolvi a Lia, assistente de recrutamento com IA integrada a WhatsApp (Chatwoot), OpenRouter API e Supabase. Tive que diagnosticar e corrigir um bug de produção onde o agente “alucinava” confirmações de ações que nĂŁo tinha executado resolvi movendo lĂłgica determinĂstica para nĂłs nativos do n8n e adicionando validação pĂłs-execução.
- ConstruĂ pipelines de triagem com AI Agent estruturado (output em JSON), incluindo debugging de nĂłs com respostas de API em formatos inesperados (markdown envolvendo JSON, mismatches de tipo de nĂł).
- Tenho experiĂŞncia com arquitetura multi-agente mais robusta (LangGraph, FastAPI) o que me ajuda a entender fluxos complexos de AI agent mesmo quando construĂdos sĂł em low-code.
Como eu faria a auditoria:
- Mapeamento: leitura completa do workflow, documentando cada nó, trigger, credencial e dependência externa (sem alterar nada em produção).
- Tracing de execuções: analisar logs/execuções passadas (sucesso e falha) para identificar onde o fluxo quebra ou se comporta de forma inconsistente.
- Ambiente de staging: replicar o workflow em sandbox com credenciais de teste antes de qualquer mudança.
- Correções incrementais: error handling, retry logic, logging e fallback, testados isoladamente por trecho do fluxo.
- Test cases: criar casos de teste cobrindo os cenários crĂticos (falha de API, timeout, resposta inesperada do LLM).
- Documentação e comprovação: relatĂłrio com o que foi alterado, evidĂŞncia de teste (prints/logs de execução) antes de considerar qualquer etapa concluĂda.
Fico à disposição para conversar mais sobre o escopo e prazo.
Upfront: I don’t speak Indonesian, so if that’s non-negotiable, ignore this and good luck with the search.
If English works, this is the kind of work I do. How I’d approach an audit like yours: read the workflows and map every external call and its failure mode first, then reproduce the failures in a staging copy rather than touching production. Fixes go in one at a time, each proven with a before and after execution log, so “fixed” is something you can see instead of something I claim.
Deliverables would be the fixed workflows, a dedicated error workflow that alerts with enough context to know what broke without opening n8n, retries with backoff on the external calls, and a written note per change.
Happy to send examples of similar work. Either way, the evidence-before-claiming-completion standard you wrote is the right one, and I’d hold to it.
Hi Ady, I realize you’re looking for someone who speaks Indonesian. While I don’t speak it fluently, I do have experience auditing and improving production n8n workflows involving AI agents, APIs, webhooks, error handling and workflow reliability. I’ll send you a DM in case my technical background is helpful.
Hello Ady. I’m Misha, an n8n and AI automation developer. I understand your production workflows are unstable and need a careful audit without risking the running system. I would start by replicating your environment in a staging sandbox to trace each workflow—Telegram, WhatsApp, AI agent calls, Google Drive, and webhooks—identifying where error handling, fallbacks, and logging are missing or failing. I would fix each failure point systematically, add retry logic and credential security checks, then run test cases with documented evidence before marking anything complete. My approach keeps your production untouched until every fix is proven solid. I speak Indonesian and can communicate the changes clearly. Portfolio: https://mikedevai.netlify.app/ | Telegram: https://t.me/hely_chatbots | WhatsApp: +375293761570