Manager, Forward Deployed Engineering

About this role

Who We Are

Artefact is an applied AI company. We build systems that run in production, in our clients' own cloud, and keep running after we leave. We are present in 27 countries across all continents with a team of 2,500 employees and offer the most comprehensive set of AI & data solutions per industry, built on deep data science and cutting-edge AI technologies, delivering AI projects at scale in all industry sectors. From strategy to operations, we partner with 1000+ clients, including some of the world’s top 300 brands.

The Role

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.

What You'll Do

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.