• Provides, guides and uses professional concepts in developing resolution to critical issues. • Train and fine-tune models using LoRA and other parameter-efficient techniques • Design, train, and evaluate multimodal models from scratch • Apply reinforcement learning techniques to model development and optimization • Experience with audio/speech ML tasks such as speaker diarization and speaker identification • Deploy, debug, and maintain distributed systems in cloud environments • Optimize inference pipelines using tools and techniques such as TensorRT, quantization, and pruning • Monitor and maintain models in production, including handling data drift and system failures • Perform data gathering, cleaning, and curation to build high-quality training and evaluation datasets • Comfortable working with a gated, continuous deployment workflow that requires unit, component, and integration tests • Applies extensive knowledge of theories, practices and design matters. • Works on issues that impact program success or addresses future concepts and products. Implementation of solutions requires a longer-term view which impacts strategic goals and objectives spanning several functions. • Exercise a wide latitude in determining objectives and approaches to critical assignments. • Knowledge of media management strategies and distribution workflows • Work closely with Product and Operations to design and optimize workflows • Apply broad expertise and knowledge in highly specialized fields or several related disciplines • Contributes to development of company objectives across and principles to achieve goals • Work on significant and unique issues where analysis of situations or data requires an evaluation of intangibles

• Translate ambiguous product or business requirements into clear technical specifications, system designs, and validation plans. • Build, maintain, and scale AI-powered production systems and agentic applications. • Design workflows where LLMs, VLMs, tools, APIs, retrieval systems, and traditional microservices work together reliably. • Use AI-assisted development tools while maintaining code quality, security, performance, and long-term maintainability. • Prevent AI-generated code bloat, dead code, and unnecessary complexity. • Build observability, evaluation, testing, and monitoring systems for AI-generated outputs. • Optimize prompts, model selection, workflows, and inference cost. • Apply Responsible AI principles including safety, privacy, fairness, transparency, and accountability. • Work with cross-functional teams across product, engineering, security, data, and design. • Mentor engineers to move from implementation-heavy development to system orchestration and AI-first engineering practices.

• Build workflows that simplify automation through prompt-based inputs. • Integrate AI capabilities into actions like Email, SMS, and Code execution. • Implement AI-driven recipe and template suggestions based on user prompts. • Create tools for building custom AI agents to handle automation tasks. • Develop advanced AI features like Data Extraction, Text-to-Voice conversion, and Data Analysis. • Work with Retrieval-Augmented Generation (RAG) systems and knowledge bases to enable intelligent, context-aware workflows. • Optimize APIs to handle millions of hits monthly with low latency. • Mentor junior engineers and lead AI-driven projects.

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.