Senior Forward Deployed Engineer (Gemini Enterprise)

You will be the team's reference for Google's enterprise AI platform.

Design and build agents with Gemini models, Vertex AI, the Agent Development Kit (ADK), and Agent Engine.

Implement and configure Gemini Enterprise for clients: Agent Designer for natural-language and trigger-based agents, the Inbox for managing long-running agents at scale, and agent sandboxes for autonomous problem-solving.

Connect Gemini Enterprise to the client's application landscape through first-party and partner connectors, with proper permissions, governance, and auditability.

Build grounded, retrieval-backed applications with Vertex AI Search and RAG Engine, grounding with Google Search, and BigQuery as the data backbone.

Implement agent interoperability through the A2A protocol and MCP.

Track Google's releases closely and translate new capabilities into client value quickly.

Our standard is production quality: systems that are reliable, monitored, and maintainable.

Write evaluation suites and regression tests for LLM-powered features, and monitor cost, latency, and quality in production.

Apply solid engineering practice: version control, code review, automated testing, CI/CD, and observability.

Deploy on cloud infrastructure (GCP, Azure, or AWS) using containers, serverless, and infrastructure-as-code.

Build and maintain the data pipelines that feed AI systems, across warehouses, lakehouses, and vector stores.

Our engineers work AI-natively and represent Artefact directly with clients.

Use agentic coding tools (Claude Code, Gemini CLI, Codex, Cursor) daily, with good judgment about verification and review.

Communicate progress, trade-offs, and blockers clearly to clients and project leads.

Support pre-sales when needed: scope solutions, build demos, and estimate effort with our partnership and consulting teams.

Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards.

Required Experience

3–5 years of experience in software engineering or data engineering, with extensive hands-on use of AI tools and LLM-based development over the past year (professional projects, internal initiatives, or substantial personal builds).

Strong hands-on experience with the Google AI stack: Gemini models, Vertex AI, and ideally Gemini Enterprise or ADK — ideally with experience taking at least one solution to production on GCP.

Strong programming skills in Python and TypeScript/JavaScript, and experience building and consuming APIs.

Experience with front-end development (React or similar) and at least one backend framework.

Hands-on experience with RAG, embeddings, and vector search, and with at least one agentic framework (Google ADK, LangGraph/LangChain).

Strong working experience with GCP; Azure or AWS is a plus.

Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor.

Experience building and maintaining data pipelines.

Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience.

A Google Cloud certification is a strong differentiator at application. If you do not hold one yet, obtaining it within your first 2 months in the role is a requirement — Artefact sponsors the exam and gives you time to prepare.

Google Cloud Professional Machine Learning Engineer (preferred), covering Vertex AI, generative AI, and production ML.

Google Cloud Generative AI Leader is valued as a foundation, complemented by hands-on Vertex AI / Gemini Enterprise delivery experience.

Experience with MCP servers, multi-agent patterns, or LLM evaluation tooling (LangSmith, Langfuse, promptfoo).

Experience with Terraform or CI/CD pipelines.

Experience with GCP, BigQuery, or Google Workspace integrations alongside Gemini Enterprise.

A strong candidate will bring:

Deep expertise in Gemini Enterprise and the Google AI stack, combined with breadth across the full stack

Owns features end to end, from interface to infrastructure

Cares about evaluation and reliability, not just the happy path

Communicates clearly with clients in demos, documents, and code review

Client-facing mindset: understands client needs and translates business requirements into technical solutions

Learns new tools and models fast, and shares what works

At Artefact, data and AI are not abstract strategy topics. They are tools for creating business value, improving organizations, and helping people make better decisions.

You will join a global community of data and AI experts who combine consulting, engineering, data science, marketing, and technology expertise. You will work on complex, high-impact problems with leading organizations and help shape how enterprises adopt AI responsibly and effectively.

We value action, collaboration, learning, client trust, and shared knowledge. We believe that technology only matters when it is used, adopted, and translated into impact.

Our values aren't decorations on a wall — they're how we actually work:

There is always a way — We're builders and problem-solvers. An idea only counts if it gets executed.

Client trust is won in the field — We show up, sleeves rolled up, working side by side with our clients.

If it's not used, it's useless — We build for adoption and impact, not for slide decks.

If it's not shared, we're not done — Knowledge shared compounds. We invest in each other's growth.

We learn every day — In a field that moves this fast, standing still means falling behind. We embrace the challenge.