• Own the production recommendation infrastructure: maintain and improve the systems that serve personalized content to millions of users, ensuring reliability, low latency, and scalability as the catalog and user base grow. • Research and prototype advanced recommendation algorithms: explore newer approaches - deep learning-based models, contextual bandits, session-based recommendations, graph-based methods - evaluate their potential, and run controlled experiments to validate uplift before production. • Produce ML models and pipelines: take prototypes (from yourself or from the team's Data Scientist) and turn them into production-grade, monitored, maintainable features integrated into the live recommendation engine. • Design scalable infrastructure: anticipate bottlenecks and design systems that can handle larger catalogs, more complex segmentations, and higher traffic - including serving layer optimization, caching strategies, and pipeline orchestration. • Build and maintain data pipelines in DBT and Databricks, ensuring clean transformations, data quality, and robust experimentation frameworks that the team can rely on. • Monitor model health in production: define retraining strategies, detect drift, and ensure recommendation quality is measured and maintained over time. • Collaborate closely with the Data Scientist and Senior Analyst to translate statistical insights and business requirements into engineering decisions.

• Design, develop, and deploy AI/ML solutions to modernize core pharmacy platforms, with a focus on scalability, reliability, performance, and security • Leverage Generative AI, LLMs, and agentic AI frameworks to automate and enhance pharmacy workflows and decision-making processes • Collaborate with business and technical stakeholders to understand pharmacy domain requirements and translate them into robust AI/ML-driven technical solutions • Contribute to architecture and technical design of AI/ML pipelines, including model selection, data integration, and deployment patterns • Establish and maintain AI/ML engineering standards, best practices, and quality assurance processes • Conduct code reviews, provide constructive feedback, and mentor engineers on modern AI/ML engineering practices • Partner with cross-functional teams including data scientists, product managers, platform engineers, and pharmacy domain experts • Translate legacy system modernization needs into scalable AI applications that enhance products, workflows, and operational efficiency • Monitor and optimize AI/ML model performance, resource utilization, and platform reliability • Collaborate with DevOps and infrastructure teams to ensure secure, scalable deployment and operations of AI/ML solutions • Stay current with advancements in AI/ML frameworks, Generative AI, LLMs, and pharmacy technology modernization

Technical · Fully Remote Senior Machine Learning Engineer / Tech Lead - AI & ML Are you up to the challenge of building the next generation of Machine Learning products on the cloud with us? We have an international, fully remote team working on creating the best Kubernetes-first, innovative virtualization platform around, and we'd like you to join as a senior engineer and team lead. In this role, you will be bringing new products to life, working with cutting-edge technology, and making a daily difference supporting users by giving them a platform on which to build awesome new things. You will get fully involved with the inner workings of Artificial Intelligence, Kubernetes, virtualization and cloud computing, with opportunities to contribute back to open-source projects and participate at industry conferences around the world. You won't just ship code: you'll join a supportive team that values your perspective and ideas from day one. Responsibilities: - Manage the development and maintenance of scalable, efficient code for the Machine Learning components of the Civo Cloud Platform - Ensure code quality, performance, and reliability through comprehensive testing and optimization - Collaborate with designers, product managers, and other engineers to translate requirements into technical solutions - Improve team performance through implementing best practices, refactoring, optimizing and improving the documentation of code and the application of best practices - Participate in code reviews, providing constructive feedback and fostering a collaborative environment - Stay up-to-date with the latest Machine Learning trends, libraries, and tools - Troubleshoot and debug complex technical issues Requirements: - Bachelor’s degree in Computer Science, Engineering or a related field, or proven related professional experience - 4+ years of professional experience developing, deploying and optimising Machine Learning Solutions - 2+ years of running large applications and systems in production environments - 2+ Experience with containerization technologies (Docker, Kubernetes) - Proven Experience training Machine Learning Models of any modality - Experience building and designing Machine Learning Pipelines or workflows (MLOps) - Experience managing complex Kubernetes Deployments - Experience scaling GPU Machine learning deployments and managing GPU Clusters - Strong written and verbal communication skills - Experience leading complex software development projects Nice to Have: - Experience in a software development management or leadership roles - Experience using Machine Learning monitoring tools - Experience enforcing coding standards and practices - Experience working in asynchronous agile software teams - Experience working in fully remote organizations - Experience with open source Machine Learning projects Why Join Civo: - Competitive compensation and benefits package - 4-day week company (unless attending an event) - Uncapped holiday - Collaborative and inclusive culture that values diversity and creativity - Opportunity to work with a dynamic and innovative team in the fast-growing cloud industry Department Technical Remote status Fully Remote

• Projetar e implementar pipelines de validação de dados e avaliação de modelos em ambiente cloud (AWS); • Integrar dados e métricas de avaliação em fluxos automatizados e auditáveis; • Modularizar pipelines para facilitar reuso, manutenção e escalabilidade; • Trabalhar em parceria com times de Data Science, Engenharia de Dados e Produto; • Garantir boas práticas de versionamento, logging, monitoramento e testes automatizados; • Propor melhorias contínuas em arquitetura de dados e processos de validação; • Apoiar deploy e acompanhamento de modelos em produção; • Atuar na análise de performance, drift e custos dos modelos implementados.