CTO AI Solutions Builder

Description Quantum Machines is a global leader in quantum computing control systems. Our hardware and software for instruction-based quantum control are changing how quantum computers are built and operated, and we work with the most advanced quantum labs and companies in the world. As the field scales, we are building a team that shapes where quantum technology goes next. The CTO organization brings together the groups that define the product and carry it to customers: Product Management owns what gets built and why, Architecture and Product Solutions translate that into deployable systems through technical pre-sales and solution design, and Customer Success & Support owns deployment, adoption, and the operational health of every installation. AI is already changing how our teams work, but mostly through isolated experiments that stay with the person who built them. We are hiring a hands-on builder to change that: to design and ship AI-enabled solutions — agents, custom skills, automations and internal applications — that remove real manual effort from the CTO organization and from the experience we give our customers. This is a new position and a build role. There is no system to inherit; you are creating one. You will work on top of the infrastructure designed and managed by our IT and DevOps teams, owning the application layer: selecting the use cases, encoding the domain logic, building the solution, and then owning whether people actually use it. Your first and largest build is knowledge management. Our product and technical knowledge sits scattered across tools, teams and heads, and keeping it current is manual work nobody has time for. The solution serves both audiences: internal teams who need to find and trust what we know, and customers who rely on our published technical content. You will build the AI-enabled way we solve that, then apply the same approach to the next problem. You will not be the only AI builder at QM — Marketing has a counterpart focused on GTM systems and revenue-facing applications. You will work in different contexts and are expected to collaborate, share components and avoid duplicating each other's work. Main responsibilities • Build and deploy AI applications, agents and skills. Architect, code, deploy and maintain agents, custom skills, automated workflows and internal applications on the environments IT and DevOps manage. • Solve knowledge management. Select the tooling and build the system behind it: a content lifecycle with named owners, review cadence, refresh audits and deprecation; findability across internal and customer-facing content; and the role-specific technical onboarding curriculum for the CTO organization. Product Operations owns what is true about the product; you build the system that keeps it current, findable and trustworthy. • Find and prioritize the use cases. Work across CS, Support, Product and the wider CTO organization to identify where manual effort is real, judge whether AI is genuinely the right tool, and prioritize by impact rather than novelty. • Manage data and integrations. Connect the business systems, data warehouse, APIs and vector stores that power reliable retrieval and automation across those solutions. • Treat prompts and agents as code. Apply software engineering practice — versioning, output validation, error handling, monitoring — to prompts, skills and multi-agent workflows, so what you ship stays reliable after you move on to the next build. • Own adoption. Measure usage and outcome, not delivery. Run the enablement, write the practical guidance, and grow the number of people who can build and use these tools competently. • Partner with IT and DevOps. Define technical and hosting requirements with them, deploy inside corporate security and data-governance standards, and help set the practical standards for how AI is used at QM. Requirements • A builder, not a maintainer. You take ownership of greenfield problems without waiting for a spec, assess friction points yourself, push back on low-value requests, and ship real solutions. • Technically grounded, business driven. You think in system architecture, data flow and security, but you measure yourself on operational impact rather than elegance. • Genuinely AI-curious. You actively work with emerging models, SDKs, vector databases and agent frameworks, and you use AI to prototype and ship faster. • A translator. You can sit with a non-technical team, understand their work well enough to encode it, and come back with something they will actually use — and you can hold a requirements conversation with IT and Security in their own language. • A structurer. You can take messy expert knowledge and shape it into something a system, and another person, can work with. Experience Must • 3+ years of hands-on software development, application engineering or data engineering, with production tools you actually shipped. • Strong proficiency in Python and/or TypeScript/JavaScript, including reading, debugging and auditing code. • Practical experience building with LLMs: prompt and context design, agents or agentic workflows, and integrating models into real systems through APIs. • Solid foundation in SQL and data modelling, REST APIs and webhooks; experience with a data warehouse such as Snowflake. • Enough infrastructure and security literacy to articulate requirements to IT and DevOps and build safely within their guidelines. • Fluent English, written and verbal. Advantage • RAG and retrieval systems over company content, vector databases (Pinecone, Qdrant, Chroma or similar), or agent frameworks (LangChain, LlamaIndex, CrewAI). • Knowledge base and documentation platforms (Confluence, Guru, Document360 or similar). • Business systems and their data — Salesforce, Jira, Slack, Microsoft 365 — and automation engines (Workato, n8n, Make). • Academic background in Computer Science or Software Engineering. • Prior experience in a B2B deep-tech, hardware or scientific-instrumentation company.