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Outcomes Architect [Contract]

2d ago
N

Notion

Notion is seeking an Outcomes Architect to drive workflow adoption and value realization for a portfolio of named accounts. This role is crucial for ensuring customers understand how to achieve desired outcomes using Notion's AI workspace. You will directly contribute to Net Revenue Retention (NRR) by fostering predictable renewal and expansion pipelines and promoting proactive product adoption. A key aspect of this position involves partnering with Solutions Consultants to develop strategies for account expansion and renewal, including managing commercial opportunities for flat and at-risk renewals. You will also define and maintain mutual success plans, guiding customers through their AI transformation and demonstrating ROI through periodic business reviews. Facilitating use-case consultations and identifying new workflow opportunities to increase value with Notion are also core responsibilities. Additionally, you will identify needs for Services engagement for complex implementations and collaborate with the Services team to ensure successful outcomes. A deep understanding of Notion's product and the broader AI ecosystem is essential to help customers navigate new ways of working and build durable systems within Notion. You will also help build and deliver Outcomes Architect (OA) programs that generate customer and business outcomes across Notion's user base.

Sydney, Australia onsite Contract
AI Agents

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What to know about the AI and engineering roles listed here.

What counts as an AI Engineering job?

AiRagJobs lists engineering roles that build, deploy, or operate AI/ML systems — including LLM engineering, RAG (retrieval-augmented generation), AI agents, prompt engineering, AI infrastructure and MLOps, model serving/inference, and fine-tuning. It excludes non-engineering functions like AI sales or marketing roles.

What is the difference between an AI Engineer and a Machine Learning Engineer?

An AI Engineer typically builds applications on top of existing models — LLM integration, RAG pipelines, agent orchestration, and prompt design. A Machine Learning Engineer more often trains, fine-tunes, or productionizes custom models. Many companies use the titles interchangeably, so search both when browsing.

Does AiRagJobs include AI infrastructure and MLOps roles?

Yes. Titles such as AI Infrastructure Engineer, ML Platform Engineer, GPU Infrastructure Engineer, Inference Engineer, MLOps Engineer, and AI Site Reliability Engineer are all covered — these roles focus on the systems that serve and scale AI models rather than building models themselves.

Are remote AI and ML jobs available?

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What skills are most in demand for AI engineering roles?

The most commonly requested skills are LLM APIs (OpenAI, Anthropic, Gemini), RAG and vector databases (Pinecone, Weaviate, Qdrant), agent frameworks (LangGraph, CrewAI, AutoGen), inference/serving tools (vLLM, Ray Serve, TensorRT-LLM), and fine-tuning. Use the skill filters on the jobs page to browse by specific technology.

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Compensation varies widely by role, seniority, and location. Where employers disclose a salary range, it is shown directly on the job listing — filter by salary range on the jobs page to narrow results to your target compensation.