- Hands on n8n experience : Production workflows or similar low code workflow engines; focus on reliability, idempotency, and failure recovery.

- Strong SQL & BigQuery skills : Data modeling, performant queries, and production grade pipelines in GCP/BigQuery (Composer/Airflow, Dataflow is a plus).

- API integration expertise : Experience with authentication (OAuth/service accounts), pagination, retries/backoff, schema evolution.

- GCP IAM experience : Designing and implementing least privilege access for GCP resources (projects, service accounts, BigQuery datasets/tables, secrets, service-to-service auth).

- LLM production usage : Experience with OpenAI, Vertex AI, or similar; prompt design, evaluation, cost/latency tradeoffs.

- Software engineering fundamentals : Skilled in Python or TypeScript/Node; comfortable with testing, code reviews, CI/CD.

- Collaboration & communication skills and ability to work cross functionally and influence decisions.

Nice to Have :

- Experience with Vertex AI, Dataform, or other GCP native ML tooling.

- Platform/enablement team experience building tools for other engineers.

Roles & Responsibilities :

- Design, build, and operate n8n workflows that orchestrate GenAI use cases end to-end, from event triggers to evaluation, monitoring, and rollout.

- Build and document custom connectors and integrations for n8n.

- Integrate with internal and external REST/GraphQL APIs, including authentication, rate limiting, and robust error handling patterns.

- Build and optimize BigQuery data models and pipelines that feed, monitor, and evaluate GenAI workflows (prompt logs, evaluation datasets, feature stores, cost dashboards).

- Implement and maintain GCP IAM and permissions models (projects, service accounts, dataset/table access, secrets) to keep GenAI workflows secure and auditable.

- Partner with AI Platforms and product teams to integrate LLM Gateway or other LLM provider APIs, including prompt design, safety/guardrail patterns, and offline evaluation loops.

- Define and instrument observability (structured logs, metrics, dashboards, alerts) for n8n + LLM workflows using Wayfair standard tooling (e.g., DataDog, logging pipelines).

- Collaborate with engineering, data science, and product stakeholders to translate fuzzy GenAI ideas into well's coped, testable workflows with clear success metrics.

- Contribute to documentation, runbooks, and reusable templates so non expert teams can safely adopt GenAI Accelerator patterns.