About The Position

As a MLOps Engineer you will work closely with our data scientists, data engineers and other stakeholders to operationalize, scale, and optimize machine learning solutions. You will be responsible for deploying, monitoring, and maintaining ML models in production, ensuring high availability, and managing infrastructure. In addition, you will be responsible for developing frameworks and tools that make the research phase more efficient for the DS as well as preparing documentation, workshops and demos for swift onboarding.

A Day In Your Life

• Collaborate with Data Scientists and Data Engineers to transform ML models into production-ready applications.
• Research, design and implement ML deployment pipelines, ensuring the robustness, scalability, and security of ML solutions.
• Monitor the performance of ML models in production, ensuring they maintain accuracy and reliability.
• Maintain and optimize cloud infrastructure and services to support ML operations.
• Implement testing, validation, and monitoring methodologies to ensure the consistency and quality of deployed ML models.
• Establish best practices and standards related to MLOps across the organization.
• Together with the Data Engineers, integrate new data sources, ensuring they are cleaned, processed, and made available for model training.
• Debug and troubleshoot ML model issues in production environments.
• Stay updated with the latest industry trends in ML, software development, and cloud operations.

What You Bring

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• At least 2 years of experience in deploying and monitoring ML models in production environments.
• Strong expertise in cloud platforms like AWS, Azure, or GCP.
• Strong expertise with container technologies like Docker and orchestration tools such as Kubernetes as well as frameworks like KubeFlow-Pipelines or MLFlow.
• Proficiency in CI/CD tools like Jenkins, Travis CI, CircleCI, or GitLab CI/CD with some Dev Ops background involving the above.
• Familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Strong programming skills in Python, and familiarity with other languages like Go.
• Good understanding of high scale distributed systems and the microservice architecture.
• Excellent communication skills with the ability to proactively lead the day-to-day communication with data science and research teams.
• Ability to write and present coherent design reviews and specs.
• Ability to move independently and lead large initiatives e2e.

Nice to have:

• Certifications in cloud platforms or MLOps-related technologies.
• Familiarity with data versioning tools like DVC or Deltalake.
• Experience in big data technologies like Spark.

Perks

• Stock options
• Paid parental leave
• Flex PTO
• Hybrid 4-day work week, every other week