• Contribute across the full software stack to deliver user journeys to production, owning implementation quality from architecture to release • Design and build agents that apply prompts, context, tools, and model reasoning to fulfill user journeys, generate content, and automate processes • Apply sound software engineering and system design principles to produce solutions that are observable, maintainable, scalable, and production ready • Validate solutions against functional requirements using both traditional QA methods, model-based evals, and agent tracing to ensure quality is measurable and reproducible • Incorporate agentic software engineering techniques into the development workflow across the SDLC, from planning, design, implementation, testing, and maintenance • Drive product definition with designers and PMs: ideate, explore, and prototype with stakeholders, then cut down possible options to arrive at deliverable, but well-rounded solutions • Wholly conceive and ship features & MVP iterations early in the product definition phase that directly advance high-visibility organizational goals • Strike the right balance between investment in UX quality and tending to practical business needs through the product development process. • Partner with other engineering stakeholders to identify what technology makes possible, and what makes implementing a given product or feature hard. Bring that perspective into project scoping and prioritization • Share knowledge, techniques, and agentic software engineering practices in async formats and dedicated venues, like technical discussions and team meetings • Champion the use of AI solutions for engineering and product tasks, accelerate product velocity and team’s execution

• You are not a developer who sits in a corner and works through tickets. You go to the client, understand their problem, and build the solution. Specifically: on-site at the client. You sit with the people who have the problem. • Run bootcamps. 5-day intensive formats where you take the client team from a vague idea to a working MVP. • Build AI systems. Agent workflows, RAG pipelines, tool integrations. • Consider governance. Regulated environments are our focus. GDPR, BAIT/VAIT, EU AI Act.

Role Description We are looking for an engineer with practical AI/LLM experience who can build production features in Ruby on Rails. This role is for someone who has worked with AI tools, LLM APIs, prompts, retrieval/context patterns, or AI-assisted workflows - and who also has the engineering depth to turn those ideas into reliable, tested, maintainable software. - Designing and building AI/LLM-assisted product features - Integrating LLM APIs into Ruby on Rails systems - Combining AI approaches with business logic, tests, fallbacks, and production safeguards - Owning features from technical design to production - Improving reliability, accuracy, consistency, latency, cost, security, and maintainability - Writing and maintaining automated tests, including unit, integration, and E2E tests - Debugging production issues and improving system behavior over time Qualifications - Practical experience building with AI/LLM technologies, not just using AI tools casually - Strong Ruby on Rails experience, ideally at a senior level - Good judgment about when AI is useful and when simpler engineering is better - Strong backend skills, including databases, API design, performance, background jobs, and security - Ability to break down ambiguous problems into clear technical solutions Requirements - CI/CD, background jobs, or AWS experience (nice to have) - Experience with text-processing, localization, translation, or multilingual products (nice to have) Benefits - Stable & self-funded: Founded in 2007, profitable, and independent. - Industry leaders: Creators of WPML, the #1 multilingual plugin for WordPress, used on 1.5M+ sites - Fully remote, async-friendly: Work from anywhere within EMEA hours - Engineering-driven culture: Ownership, autonomy, testing, and thoughtful problem-solving - Fair compensation and great benefits: Designed to support your well-being both inside and outside of work.

• Join Wiremind's emerging GenAI team under the supervision of our Lead GenAI Engineer • Contribute to developing LLM-powered features to help airlines and railways make better pricing and operational decisions • Participate in systematic evaluation, iterative improvement, model selection, and structured experimentation • Design and run experiments to test different approaches • Engage in prompt engineering through systematic testing and refinement • Develop evaluation frameworks and metrics aligned with expert preferences • Benchmark LLM models to find optimal cost/performance tradeoffs • Analyze failure modes and edge cases in LLM outputs • Document and share findings with the broader engineering and product teams