{"id":144873,"url":"https://alion.io/job/artefact-senior-forward-deployed-engineer-gemini-enterprise","title":"Senior Forward Deployed Engineer (Gemini Enterprise)","company":{"id":53062,"name":"Artefact","domain":"artefact.com","url":"https://alion.io/company/artefact-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":116,"ghost_share":0.336,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":124,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Solutions","role_family":"Solutions","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Montreal, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":68000,"max_usd":138000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":39},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A2A","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Go","optional":false},{"name":"Google ADK","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Hybrid Search","optional":false},{"name":"JavaScript","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"TypeScript","optional":false},{"name":"Vertex AI","optional":false},{"name":"Google Workspace","optional":true},{"name":"Langfuse","optional":true},{"name":"LangSmith","optional":true},{"name":"Machine Learning","optional":true},{"name":"Promptfoo","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-08-15T00:23:37Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-22T14:40:38Z","board_verified":true,"closed_at":null,"days_open":68,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":68},"description":"About Artefact\nArtefact 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.\nWe 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.\nAs 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.\nThe Role\nArtefact 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.\nYou 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.\nThis 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.\nYou will work closely with our clients, with direct exposure from the start, and you will support the professional development of the engineers around you.\nWhat You'll Do\nBuild Full-Stack AI Applications, End to End\nYou will build AI products across the entire stack, from interface to infrastructure.\nDevelop user-facing interfaces in TypeScript/React and the backend services and APIs behind them in Python or Node.\nImplement agentic behavior: orchestration, tool and function calling, memory, and guardrails.\nBuild retrieval-augmented generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid search.\nConnect AI systems to enterprise data and applications via APIs, semantic layers, and protocols such as MCP.\nGo Deep on Gemini Enterprise and the Google AI Stack\nYou will be the team's reference for Google's enterprise AI platform.\nDesign and build agents with Gemini models, Vertex AI, the Agent Development Kit (ADK), and Agent Engine.\nImplement 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.\nConnect Gemini Enterprise to the client's application landscape through first-party and partner connectors, with proper permissions, governance, and auditability.\nBuild grounded, retrieval-backed applications with Vertex AI Search and RAG Engine, grounding with Google Search, and BigQuery as the data backbone.\nImplement agent interoperability through the A2A protocol and MCP.\nTrack Google's releases closely and translate new capabilities into client value quickly.\nMake AI Systems Production-Grade\nOur standard is production quality: systems that are reliable, monitored, and maintainable.\nWrite evaluation suites and regression tests for LLM-powered features, and monitor cost, latency, and quality in production.\nApply solid engineering practice: version control, code review, automated testing, CI/CD, and observability.\nDeploy on cloud infrastructure (GCP, Azure, or AWS) using containers, serverless, and infrastructure-as-code.\nBuild and maintain the data pipelines that feed AI systems, across warehouses, lakehouses, and vector stores.\nWork AI-Natively and Client-Facing\nOur engineers work AI-natively and represent Artefact directly with clients.\nUse agentic coding tools (Claude Code, Gemini CLI, Codex, Cursor) daily, with good judgment about verification and review.\nCommunicate progress, trade-offs, and blockers clearly to clients and project leads.\nSupport pre-sales when needed: scope solutions, build demos, and estimate effort with our partnership and consulting teams.\nMentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards.\nWhat We're Looking For\nRequired Experience\n3-5 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).\nStrong 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.\nStrong programming skills in Python and TypeScript/JavaScript, and experience building and consuming APIs.\nExperience with front-end development (React or similar) and at least one backend framework.\nHands-on experience with RAG, embeddings, and vector search, and with at least one agentic framework (Google ADK, LangGraph/LangChain).\nStrong working experience with GCP; Azure or AWS is a plus.\nFluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor.\nExperience building and maintaining data pipelines.\nBachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience.\nCertifications\nA 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.\nGoogle Cloud Professional Machine Learning Engineer (preferred), covering Vertex AI, generative AI, and production ML.\nGoogle Cloud Generative AI Leader is valued as a foundation, complemented by hands-on Vertex AI / Gemini Enterprise delivery experience.\nPreferred Experience\nExperience with MCP servers, multi-agent patterns, or LLM evaluation tooling (LangSmith, Langfuse, promptfoo).\nExperience with Terraform or CI/CD pipelines.\nExperience with GCP, BigQuery, or Google Workspace integrations alongside Gemini Enterprise.\nKey Capabilities\nA strong candidate will bring:\nDeep expertise in Gemini Enterprise and the Google AI stack, combined with breadth across the full stack\nOwns features end to end, from interface to infrastructure\nCares about evaluation and reliability, not just the happy path\nCommunicates clearly with clients in demos, documents, and code review\nClient-facing mindset: understands client needs and translates business requirements into technical solutions\nLearns new tools and models fast, and shares what works\nWhy Join Artefact\nAt Artefact, data and AI are not abstract strategy topics. They are tools for creating business value, improving organizations, and helping people make better decisions.\nYou 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.\nWe value action, collaboration, learning, client trust, and shared knowledge. We believe that technology only matters when it is used, adopted, and translated into impact.\nOur values aren't decorations on a wall - they're how we actually work:\nThere is always a way - We're builders and problem-solvers. An idea only counts if it gets executed.\nClient trust is won in the field - We show up, sleeves rolled up, working side by side with our clients.\nIf it's not used, it's useless - We build for adoption and impact, not for slide decks.\nIf it's not shared, we're not done - Knowledge shared compounds. We invest in each other's growth.\n We learn every day - In a field that moves this fast, standing still means falling behind. 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