Artefact
Manager, AI & Data Consulting
$140K–$160K
135 W 26th Street, New York, NY 10001 +3 · Manager · Full-time · Posted Aug 19, 2026
- Location
- 135 W 26th Street, New York, NY 10001
Montréal, Quebec, Canada
Montréal, Quebec, Canada
North America - Experience
- 5–7 years of full-time, client-facing experience in consulting, technology, media, or a related field.
- Education
- Undergraduate degree, preferably in business, technology, or another field related to the work.
About the job
Leads client-facing AI and data consulting projects from planning through delivery, builds client relationships, develops team members, and prototypes AI-enabled solutions.
What you'll do
- Manage projects end to end, including workstreams, team objectives, risks, deliverables, and executive communication.
- Translate client needs into AI and data recommendations; prototype ideas using AI tools and help clients connect AI to their data and systems.
- Coach and evaluate team members, support client relationship growth, and contribute to proposals and business development.
What they ask for
- 5–7 years of full-time, client-facing work experience in consulting, technology, media, or a related field.
- Autonomous project and small-team management experience in a client environment.
- Daily practical use of AI tools and hands-on prototyping with agentic coding tools; comfort with the terminal and verifying AI output.
- Working knowledge of at least one named AI platform ecosystem and core GenAI concepts; excellent communication skills.
Nice to have
- Strategy consulting and/or digital marketing background; SQL or Python skills for hands-on analysis.
- Experience contributing to proposals and business development; intercultural experience across global teams.
Pay
USD $140,000 - $160,000 base salary (NYC location)
Tools
Claude Code, Gemini CLI, Codex, Google (Gemini), Anthropic (Claude), OpenAI, RAG, MCP
AI focus
Uses AI tools in daily work and prototypes with agentic coding tools; develops strategic AI recommendations and helps clients connect AI with their data, documents, and systems using concepts such as RAG, semantic layers, and MCP.
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