Senior Forward Deployed AI Engineer (Gemini Enterprise)

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).

Professional English proficiency (C1/C2 minimum) — mandatory. You will work daily with international clients and colleagues.

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.

Certifications

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.

Preferred 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.

Key Capabilities

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

A Senior Deployed AI Engineer specialized in Gemini Enterprise and the Google AI stack: an engineer who works embedded with our clients and takes AI products from idea to production.

You'll design and build the interfaces, services, and agentic systems at the heart of our client work: conversational apps over enterprise data, agents automating workflows, and the pipelines supporting them. You own components end to end: front end, service, data, deployment, and evals.

Build Full-Stack AI Applications, End to End

Develop interfaces in TypeScript/React and backend services/APIs in Python or Node.

Implement agentic behavior: orchestration, tool/function calling, memory, guardrails.

Build RAG pipelines: ingestion, chunking, embeddings, vector/hybrid search.

Connect AI systems to enterprise data via APIs, semantic layers, and MCP.

Go Deep on Gemini Enterprise and the Google AI Stack

Build agents with Gemini models, Vertex AI, ADK, and Agent Engine.

Configure Gemini Enterprise: Agent Designer, the Inbox, and agent sandboxes.

Connect Gemini Enterprise to client systems via connectors, with proper permissions.

Build grounded apps with Vertex AI Search, RAG Engine, Search grounding, and BigQuery.

Implement interoperability via A2A and MCP.

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

Make AI Systems Production-Grade

Write evals and regression tests; monitor cost, latency, and quality.

Apply solid practice: version control, review, testing, CI/CD, observability.

Deploy on GCP/Azure/AWS using containers, serverless, and infra-as-code.

Build and maintain data pipelines feeding AI systems.

Work AI-Natively and Client-Facing

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

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

Support pre-sales: scope solutions, build demos, estimate effort with partners.

Mentor engineers; contribute to accelerators and engineering standards.

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.