Customer Engineer

Customer EngineerEspoo, FinlandAbout ConfidentialMind ConfidentialMind is a private AI platform for enterprises that enables organizations to deploy and run large language models securely with their own data inside their own environments. Our platform provides ready-to-use AI systems including LLM serving, RAG, and agents, all accessible through OpenAI-compatible APIs and deployed entirely within the customer's environment: on-premises, private cloud, air-gapped, public cloud, or the edge. We serve customers in healthcare, finance, the public sector, and other regulated industries where data sovereignty is a hard requirement. We work with partners including Dell, Fujitsu, NVIDIA, and Red Hat to deliver secure AI infrastructure across deployment environments. Our team is based in Espoo, Finland and San Francisco, California. We are a small, focused team of infrastructure and AI engineers building the foundation for private, self-hosted enterprise AI.About the role We're looking for a Customer Engineer to work directly with our customers to turn a deployed platform into real, adopted use cases. You'll sit alongside customer teams to identify where the ConfidentialMind platform should be applied, help them build it, and make sure what gets built gets used.This isn't a hand-off-and-move-on role. You'll own the adoption outcome for your accounts. That means working closely with the customer to shape use cases, with product development to make sure the platform evolves in the right direction, and with the wider team to spot opportunities to expand what the customer is doing with us.You don't need to be an infrastructure expert, but you do need to know how to make generative AI work in practice. You should be able to look at a customer's problem and judge whether an LLM is the right tool, design a solution around it (prompting, retrieval, agents, or a combination), and tell when the output is good enough for production. In this role, you will Drive and implement GenAI use cases in close collaboration with the customer, from initial idea through to production use Design, prototype and iterate on GenAI solutions with the customer's real data: shaping prompts, setting up retrieval, choosing models, and measuring whether the results are good enough to rely on Own adoption of the ConfidentialMind platform within your accounts, making sure what's deployed is being used and delivering value Act as the voice of the customer internally, capturing feedback and feeding it back to product development in a form they can act on Spot opportunities to expand a customer's usage and help the sales team position additional services when it genuinely helps the customer Spend meaningful time on customer premises, including international travel, working alongside customer teams rather than only over calls Contribute to internal playbooks on use case design and adoption as we build out this functionYou might thrive in this role if You'd rather be in the room with a customer solving their actual problem than managing a ticket queue You bring a problem solver's mindset: you understand the customer's actual problem before proposing a solution, and you stay accountable for the outcome, not just the deliverable You have built LLM based applied AI solutions yourself and know where they fall short: hallucinations, retrieval that misses the relevant document, prompts that work in a demo but not on real data. You enjoy closing that gap. You can hold your own in a conversation about GPUs, APIs, Kubernetes, and how LLMs are deployed, even if you're not the one architecting the infrastructure You get energy from seeing something you helped build get used, not just shipped You communicate clearly and directly, in writing and out loud, with both technical and non-technical people You're comfortable in a small team where processes are still being built, not inherited You don't mind being on the road when the work calls for it Qualifications 2–4 years in a customer-facing technical role such as customer engineering, solutions engineering, technical consulting, or similar Hands-on experience, or a demonstrable project, building applications with LLMs, such as RAG pipelines, agents or tool use, prompt design, and evaluating output quality Understanding of the practical trade-offs in model choice: size, latency, context length, cost, and quality Comfortable prototyping in Python or a similar language Working knowledge of cloud infrastructure and Kubernetes; hands-on production experience is a plus Comfortable with REST APIs and technical enough to work directly with customer engineering teams Excellent written and spoken English Willingness and ability to travel to customer sites as neededPracticalities Work is primarily during standard business hours.What you can expect Work on difficult problems with great people The opportunity to help build ConfidentialMind's customer engineering function from the ground up as the team scales Equity compensation An international working environment where English is the primary language