Senior MLOps Engineer

Wellhub·Remote(Brazil)
Data & Analytics
Excel

WFA Digital Insight

The demand for skilled MLOps engineers has grown significantly, with a 25% increase in job postings in the last year alone. As companies like Wellhub continue to invest in AI and machine learning, the need for experts who can bridge the gap between data science and software engineering has never been more pressing. With the rise of remote work, candidates can now access these high-demand roles from anywhere in the world. Before applying, it's essential to understand the unique challenges and opportunities that come with working in a distributed team and building scalable AI infrastructure.

Job Description

About the Role

The Senior MLOps Engineer role at Wellhub is a unique opportunity to join a team of innovators who are redefining the future of workplace wellness. As a member of the Product Development team, you will be responsible for scaling the company's AI infrastructure and building autonomous ML workflows. This is a remote position based in Brazil, offering the flexibility to work from anywhere in the country.

Wellhub is a company that values wellbeing, collaboration, and different perspectives. The team is passionate about creating a supportive environment where everyone feels comfortable taking care of themselves and finding work-life wellness. As a Senior MLOps Engineer, you will be expected to embody these values and inspire others to do the same.

The role entails working closely with distributed teams to design and implement scalable ML infrastructure, ensuring seamless integration with the company's data catalog, privacy, and governance frameworks. You will be responsible for building and maintaining the company's Kubeflow, Feast, and Spark-on-Kubernetes infrastructure, as well as collaborating with data science teams to adapt software engineering best practices to ML-specific workflows.

What You Will Do

  • Evolve and maintain the company's Kubeflow, Feast, and Spark-on-Kubernetes infrastructure to ensure it can handle increasing complexity
  • Design and implement internal tools, APIs, and abstractions to empower distributed teams to own their entire ML lifecycle
  • Collaborate with embedded data science teams to adapt software engineering best practices to ML-specific workflows
  • Drive MLOps best practices and define lifecycle requirements for LLMOps, ensuring a frictionless journey from experimental notebooks to production-grade solutions
  • Partner with infrastructure and data squads to break silos and integrate ML artifacts into the global data catalog, privacy, and governance frameworks
  • Build and maintain a cloud-native ecosystem, creating a seamless and high-performance environment for the next generation of AI-driven products
  • Scale the ecosystem to handle the increasing complexity of both traditional ML and the new wave of AI
  • Build for autonomy, designing and implementing tools and processes that enable distributed teams to work independently
  • Standardize engineering excellence, collaborating with data science teams to raise the bar for production-grade AI across the company
  • Collaborate with cross-functional teams to ensure seamless integration of ML workflows with the company's data catalog, privacy, and governance frameworks

What We Are Looking For

  • Platform-as-a-Product Mindset, treating the AI infrastructure as a product and obsessing over developer experience and continuous feedback loops
  • Collaborative Architect skills, excelling at balancing distributed team autonomy with global platform standards
  • Pragmatic Innovator, prioritizing robust, scalable production solutions while staying on top of the wave of concepts and practices
  • Systems Thinker, seeing the big picture and valuing the integration of ML infrastructure with broader data cataloging and governance
  • DevOps experience applied to ML/AI, with deep experience with CI/CD, Infrastructure as Code (Terraform/Crossplane), and Observability
  • Hands-on mastery of the Kubeflow platform, Spark engine, AWS ecosystem, and Kubernetes
  • Strong Python skills, focused on building scalable and efficient ML workflows
  • Experience working with distributed teams and building autonomous ML workflows

Nice to Have

  • Experience with other ML frameworks and tools, such as TensorFlow or PyTorch
  • Knowledge of data cataloging and governance principles, with experience integrating ML workflows with data catalogs
  • Experience working in a remote team environment, with strong communication and collaboration skills
  • Familiarity with Agile development methodologies and version control systems such as Git

Benefits and Perks

  • Competitive salary and benefits package
  • Opportunity to work with a talented team of innovators who are passionate about creating a healthier, more balanced world
  • Flexible working hours and remote work options, with the ability to work from anywhere in Brazil
  • Access to cutting-edge technology and tools, with opportunities for professional growth and development
  • Comprehensive health and wellness programs, with a focus on supporting the wellbeing of employees and their families
  • Generous PTO and holiday package, with opportunities for paid time off and vacation

How to Stand Out

  • To stand out as a candidate, be prepared to provide specific examples of your experience with scalable ML infrastructure and autonomous ML workflows.
  • Make sure to highlight your skills in DevOps, Kubeflow, and Python, as well as your experience working with distributed teams.
  • When interviewing, be prepared to discuss your approach to building and maintaining scalable ML infrastructure, as well as your experience with data cataloging and governance.
  • Be sure to ask about the company culture and values, as well as the opportunities for professional growth and development.
  • Consider creating a portfolio of your work, including examples of your experience with ML frameworks and tools, to showcase your skills to potential employers.
  • Be prepared to negotiate your salary and benefits package, and don't be afraid to ask about opportunities for remote work and flexible scheduling.

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