Applied AI Engineer

LocationOur Applied AI Engineer will be an integral part of our global business systems team. This role is based remotely in the US East, the UK, Ireland, Estonia, the Netherlands, Sweden and Israel.Who We Are DoiT is a global technology company that works with cloud-driven organizations to leverage the cloud to drive business growth and innovation. We combine data, technology, and human expertise to ensure our customers operate in a well-architected and scalable state - from planning to production. Delivering DoiT Cloud Intelligence, the only solution that integrates advanced technology with human intelligence, we help our customers solve complex multicloud problems and drive efficiency.With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we work alongThe OpportunityWe are bringing the "Full Stack Builder" model to DoiT's internal operations. As an Applied AI Engineer, you are a one-person product team embedded directly into a core business function.This is not an IT support role. You are not fielding tickets. You have a dedicated product surface - your assigned department - and a clear success metric: measurable process improvement, shipped fast. You will rely on your empathy and judgment to identify the friction, and you will use modern AI tooling to build, code, and deploy the automations end-to-end.You live and breathe AI. You automate your own life for fun, you use AI agents as an extension of your own brain, and you get a genuine dopamine hit from taking a slow, manual process and turning it into a seamless, AI-driven workflow.What We're Looking For:The Solver Mindset: You can research a problem, design a solution, code it, and launch it. You use AI coding assistants heavily to multiply your own output.The AI-Native Builder: You don't just know about AI. You build with it daily. You instinctively reach for tools like Claude Code, Gemini CLI and Codex to multiply your output.You are fluent in modern AI coding environments and autonomous agents like Claude Code.Strong Software Fundamentals: You produce production-quality code, not just brittle scripts.AI Integration Experience: Hands-on experience with LLM APIs, prompt engineering, RAG, MCP, and agent-based architectures.Extreme Empathy: Translating messy, real-world business workflows into clean technical solutions requires deep listening and communication skills. You can speak "Engineering" and business language equally well.Self-Direction: There is no established playbook for this role. You will need to find the work, scope it, build it, and prove it matteredRequired Tech Stack & SkillsAI & LLM StackAI Agents: you live and work inside AI agents like Claude Code, Gemini CLI, and Codex. This is not optional and not a nice-to-have. AI-driven engineering is how we build at DoiT. You use AI agents as an extension of your brain to research, code, test, and deploy - and you can critically evaluate the output. If you're not already shipping production code through AI agents daily, this role is not for you.LLM APIs: hands-on experience with the Anthropic Claude API (primary) and/or OpenAI API, Google Gemini APIRelentlessly Current: the AI stack moves weekly, not yearly. You follow model releases, new agent frameworks, protocol changes, and tooling updates as they happen. You don't wait for a blog post summary - you read the changelog, try the beta, and know when to adopt and when to skip. Today it's MCP and Claude Code, six months from now it might be something else entirely. You'll be the first to know.MCP (Model Context Protocol): building or consuming MCP servers to connect AI agents to external tools and data sourcesAgentic Architectures: designing multi-step agent workflows with tool use, decision-making, human-in-the-loop escalation, and guardrailsCore Development StackTypeScript / JavaScript - primary language for building internal tools and automationsNext.js - for building internal-facing web applications and dashboardsPython - for data processing, scripting, and AI/ML workflowsFirestore / Firebase - primary database and backend servicesGoogle Cloud Platform - Cloud Run, Pub/Sub, IAMGit/ Github - version control and collaborative developmentProduction & ReliabilityAPI Design - RESTful APIs, webhooks, secure authentication patternsContainerization - Docker for packaging and deploying servicesCI/CD - automated testing and deployment pipelines (GitHub Actions, Google Cloud Build)Observability - logging, monitoring, and error tracking for AI-powered systemsRequired Qualifications3–5 years of professional software engineering experience, including building and deploying production applications1+ year of hands-on experience building multimodal LLM-powered applications that run in production - AI agents, agentic workflows, or LLM-integrated tools (not just prompt experimentation)Early adopter of AI-driven engineering - Claude C