Manager, Forward Deployed Engineering

Artefact is looking for a Manager, Forward Deployed Engineering, specialized in Gemini Enterprise and the Google AI stack: a hands-on technical leader who owns the delivery of AI products for our clients, from architecture through production, while building and leading the team of engineers who build them.

You will split your time between technical leadership and people leadership. You set the technical direction for one or more client engagements, stay close enough to the code and architecture to make good calls under pressure, and are the senior escalation point when things get hard — technically or with the client. At the same time, you manage a team of Deployed AI Engineers: their staffing, their growth, their day-to-day delivery, and their development into the next generation of technical leads.

This role combines deep, certified expertise in Google's enterprise AI stack (Gemini models, Vertex AI, and the Gemini Enterprise agent platform) with the leadership skills to run a delivery team and manage senior client relationships.

Lead Delivery of Full-Stack AI Applications

• Own technical delivery across one or more client engagements: architecture decisions, technical risk, and quality bar.
• Set direction on interface design (TypeScript/React), backend services (Python or Node), and the agentic and retrieval systems underneath.
• Review and unblock your team's work on orchestration, tool/function calling, memory, guardrails, and RAG pipelines.
• Make the build-vs-buy and platform calls that shape how a client's AI systems connect to their data and applications.

Be the Team's Senior Reference on Gemini Enterprise and the Google AI Stack

• Set technical standards for how the team builds with Gemini models, Vertex AI, ADK, and Agent Engine.
• Guide the team's implementation and configuration of Gemini Enterprise: Agent Designer, Inbox, agent sandboxes, and connector governance.
• Make architecture calls on Vertex AI Search, RAG Engine, grounding with Google Search, and BigQuery as the data backbone.
• Track Google's roadmap and releases, and decide when and how new capabilities get adopted into client work.

Own Production Quality and Delivery Standards

• Set and enforce the bar for evaluation suites, regression testing, and production monitoring (cost, latency, quality) across your team's engagements.
• Establish engineering practice across your team: version control, code review, CI/CD, observability.
• Make infrastructure and deployment decisions across GCP, Azure, or AWS.
• Own the health of the data pipelines feeding your team's AI systems.

Lead the Team and the Client Relationship

• Manage a team of Deployed AI Engineers: staffing, workload, performance, and career development.
• Hire, onboard, and grow engineers into strong Gemini Enterprise specialists.
• Be the senior client-facing voice for your engagements — communicating trade-offs, risk, and progress to client stakeholders and Artefact leadership.
• Support pre-sales at a senior level: scope solutions, size teams, and estimate delivery effort with partnership and consulting teams.
• Set and scale internal accelerators, reusable components, and engineering standards across engagements, not just within one team.