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About Artefact Artefact is a next-generation data and AI consulting firm dedicated to accelerating the adoption of data and AI to create measurable business impact across the full enterprise value chain. We sit at the intersection of consulting, data science, AI technologies, data engineering, and digital transformation. We do not just advise — we build, implement, and deliver results our clients can measure. Our teams bring together consultants, data scientists, data engineers, AI engineers, analysts, and digital experts to solve complex business challenges with pragmatic, production-ready solutions. As Artefact continues to grow globally, we are building a team of entrepreneurial data and AI talent who can help clients move beyond experimentation and into scalable, governed, value-generating AI adoption. The Role Artefact is looking for 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 will design and build the interfaces, services, and agentic systems at the heart of our client work, such as conversational applications over enterprise data, multi-step agents that automate business workflows, and the retrieval and data pipelines that support them. You will own your components end to end: the front end, the service behind it, the data and retrieval pipelines feeding it, the deployment, and the evaluations proving it works. This role combines deep, certified expertise in Google's enterprise AI stack (Gemini models, Vertex AI, and the Gemini Enterprise agent platform) with the ability to deliver end to end. Beyond your platform specialization, you will be expected to work confidently across the full delivery lifecycle — full-stack development, data engineering, cloud infrastructure, evaluation, and client communication. You will work closely with our clients, with direct exposure from the start, and you will support the professional development of the engineers around you. What You'll Do Build Full-Stack AI Applications, End to End You will build AI products across the entire stack, from interface to infrastructure. Develop user-facing interfaces in TypeScript/React and the backend services and APIs behind them in Python or Node. Implement agentic behavior: orchestration, tool and function calling, memory, and guardrails. Build retrieval-augmented generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid search. Connect AI systems to enterprise data and applications via APIs, semantic layers, and protocols such as MCP. Go Deep on Gemini Enterprise and the Google AI Stack 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. Make AI Systems Production-Grade 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. Work AI-Natively and Client-Facing 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. What We're Looking For 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. 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 Why Join Artefact 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.