Senior Forward Deployed 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).
• 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.
• 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