<p><strong>Your wellbeing, our mission. Join a company shaping a healthier world.</strong></p><h3>GET TO KNOW US</h3><p>At <a href="https://himalayas.app/companies/wellhub">Wellhub</a> we're revolutionizing workplace wellness. Our platform connects employees worldwide to the best partners for fitness, mindfulness, therapy, nutrition, and sleep—all in one simple subscription. Headquartered in NYC with team members in Europe, North America and South America, we’re on a mission to make every company a wellness company.</p><p>We believe work should be fulfilling, inspiring, and balanced. Here, you’ll find a team that values wellbeing, collaboration, and different perspectives, where passion and creativity push boundaries to create real impact. Your contributions will help shape a healthier, more balanced world for you and millions of people globally. </p><h3>Join us in redefining the future of wellbeing!</h3><h3>THE OPPORTUNITY</h3><p>We are hiring a <strong>Senior MLOps Engineer</strong> to our Product Development team in <strong>Brazil</strong>! This is a<strong> Remote – Brazil</strong> position, meaning you can work from anywhere within the country. Please note that this role is only open to candidates in Brazil. </p><p>Join the <strong>ML Development Lifecycle</strong> team within our Product Development (PD) organisation, where we are redefining how a global tech company leverages intelligence. We build the <strong>foundations</strong> that allow hundreds of engineers and data scientists to develop and deploy AI at scale. You will own the evolution of our <strong>cloud-native ecosystem</strong>, creating a seamless and high-performance environment for the next generation of AI-driven products.<br><br>If you are a software-minded engineer who thrives at the intersection of scalable <strong>Infrastructure and ML/AI orchestration, </strong>this is your chance to build a world-class platform that serves millions of users worldwide.</p><h3>YOUR IMPACT</h3><ul><li><strong>Scale the Ecosystem:</strong> Evolve and maintain our <strong>Kubeflow, Feast and Spark-on-Kubernetes</strong> infrastructure, ensuring it can handle the increasing complexity of both traditional ML and the new wave of AI.</li><li><strong>Build for Autonomy:</strong> Design the internal tools, APIs, and abstractions that empower distributed teams to own their entire lifecycle—transitioning our data culture from "centralised bottleneck" to <strong>"self-service excellence."</strong></li><li><strong>Standardise Engineering Excellence:</strong> Collaborate with embedded Data Science teams to adapt software engineering best practices (CI/CD, versioning, testing) to ML-specific workflows, raising the bar for production-grade AI across the company.</li><li><strong>Seamless Lifecycle Orchestration:</strong> Drive MLOps best practices and define the specific lifecycle requirements for LLMOps, ensuring a frictionless journey from experimental notebooks to robust, production-grade solutions