ai engineer for multilingual translation platforms
3 days ago
Barcelona
Wizeline is a global AI-native technology solutions provider that develops AI-powered digital products and platforms. It partners with clients to leverage data and AI, accelerate market entry, and drive business transformation. • Design, develop, and enhance scalable content and translation platforms using Java, Spring Boot, REST APIs, and microservices architectures, • Build and integrate LLM- and AI-powered translation capabilities into enterprise content-processing workflows, • Develop reliable APIs and backend services for translation requests, processing, and results, ensuring scalability, performance, and maintainability, • Integrate with AI/LLM and machine-translation providers, evaluating opportunities to improve translation quality, scalability, cost, and latency, • Design and implement event-driven architectures using technologies such as Apache Kafka to support high-volume content-processing pipelines, • Integrate translation and content services with existing enterprise platforms, APIs, and downstream systems, • Develop solutions for multilingual content processing, supporting multiple languages, content types, and international publishing requirements, • Implement error handling, retries, monitoring, logging, alerting, and other operational capabilities for reliable production systems, • Develop automated tests and validation mechanisms for translation workflow and service quality and reliability, • Improve performance and scalability as content and translation volumes increase, • Support production deployments, troubleshooting, incident resolution, and ongoing operational improvements, • Collaborate with engineering, product, and client stakeholders to define technical solutions, participate in design discussions, and translate business requirements into scalable software, • Contribute to technical documentation, code reviews, CI/CD practices, and engineering standards, • Help evolve the platform architecture to support international expansion, additional languages, and new content types, • Stay current with developments in GenAI, LLMs, machine translation, multilingual NLP, cloud technologies and distributed systems, applying relevant practices to production solutions, • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related STEM field, or equivalent practical experience, • At least 4 years of professional software engineering experience, including strong experience developing enterprise applications with Java and Spring Boot, • Strong experience designing and developing REST APIs and microservices, • Hands-on experience with cloud-based application development, preferably on Google Cloud Platform (GCP), • Practical experience with Apache Kafka or similar event-driven messaging technologies, • Experience developing or integrating LLM/AI-powered applications and APIs in production or enterprise environments, • Experience with machine translation, multilingual content processing, NLP, or related AI-driven content technologies, • Strong understanding of software engineering practices, including Git, CI/CD, automated testing, code reviews, and Agile methodologies, • Experience integrating applications with existing enterprise platforms, APIs, and third-party services, • Ability to troubleshoot production issues and contribute to monitoring, logging, performance optimization, and operational support, • Strong written and verbal English communication skills, • Будет плюсом: experience with content-management, publishing, media, or digital content platforms; experience with LLM providers, AI APIs, or machine-translation platforms such as Google Cloud Translation, Azure AI Translator, AWS Translate, or equivalent services; familiarity with Google Cloud Platform services such as GKE, Cloud Run, Pub/Sub, Cloud Storage, BigQuery, or similar; experience with multilingual NLP, translation quality evaluation, language detection, or localization workflows; familiarity with AI/LLM orchestration frameworks and tools such as LangChain, LangGraph, LangSmith, or equivalent; experience implementing observability solutions using tools such as Cloud Monitoring, Cloud Logging, Datadog, Grafana, or equivalent; experience with Docker and containerized application deployments; experience with high-volume, distributed, or latency-sensitive systems; familiarity with media or publishing workflows, content pipelines, editorial systems, or international content distribution; comfort using AI tools to optimize day-to-day work, including development, analysis, research, testing, and automation, with the ability to identify opportunities to improve engineering workflows Specific benefits are determined by employment type and location #J-18808-Ljbffr
Didac Q.Active 9 days ago
