Senior AI Platform Engineer

Your wellbeing, our mission. Join a company shaping a healthier world.

GET TO KNOW US

At Wellhub 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.

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.

Join us in redefining the future of wellbeing!

THE OPPORTUNITY

We are hiring a Senior AI Platform Engineer to our Product Development team in Brazil ! This is a Remote – Brazil position, meaning you can work from anywhere within the country. Please note that this role is only open to candidates in Brazil.

Join the ML Development Lifecycle team within our Product Development (PD) organisation, where we are redefining how a global tech company leverages intelligence. We build the foundations that allow hundreds of engineers and data scientists to develop and deploy AI at scale. You will own the evolution of our cloud-native ecosystem , creating a seamless and high-performance environment for the next generation of AI-driven products. If you are a software-minded engineer who thrives at the intersection of scalable Infrastructure and ML/AI orchestration, this is your chance to build a world-class platform that serves millions of users worldwide.

YOUR IMPACT

Scale the Ecosystem: Evolve and maintain our Kubeflow, Feast and Spark-on-Kubernetes infrastructure, ensuring it can handle the increasing complexity of both traditional ML and the new wave of AI.

Build for Autonomy: Design the internal tools, APIs, and abstractions that empower distributed teams to own their entire lifecycle—transitioning our data culture from "centralised bottleneck" to "self-service excellence."

Standardise Engineering Excellence: 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.

Seamless Lifecycle Orchestration: Drive MLOps best practices and define the specific lifecycle requirements for LLMOps, ensuring a frictionless journey from experimental notebooks to robust, production-grade solutions by providing top-notch deployment frameworks and automation.

Cross-Squad Collaboration: Partner closely with our Infrastructure and Data squads to break silos and integrate ML artifacts into our global Data Catalog, Privacy, and Governance frameworks.

Live the mission : inspire and empower others by genuinely caring for your own wellbeing and your colleagues. Bring wellbeing to the forefront of work, and create a supportive environment where everyone feels comfortable taking care of themselves, taking time off, and finding work-life wellness.

WHO YOU ARE

Platform-as-a-Product Mindset: You treat our AI infrastructure as a product, obsessing over Developer Experience (DX) and continuous feedback loops.

Collaborative Architect: You excel at balancing distributed team autonomy with global platform standards.

Pragmatic Innovator: You prioritize robust, scalable production solutions while staying on top of the wave of concepts and practices.

Systems Thinker: You see the big picture, valuing the integration of ML infrastructure with broader data cataloging and governance.

COMPETENCIES:

DevOps applied to ML/AI: Deep experience with CI/CD, Infrastructure as Code (Terraform/Crossplane), and Observability.

Kubeflow, Spark, K8 and AWS: Hands-on mastery of the Kubeflow platform, Spark engine, AWS ecosystem and Kubernetes.

Software Foundations: Strong Python skills, focused on building reusable libraries, APIs, and tooling abstractions.