- Data Science and Engineering
- India - Bangalore Sales Office
Associate Principal MLOps Engineer
ABOUT THE ROLE
Dyson Global IT is seeking an Associate Principal MLOps Engineer who will be responsible for building and maintaining the Dyson MLOps platform and setting the way for Dyson to deploy Machine Learning models to production. You will work closely with the project teams and external stakeholders to ensure that the platform meets Dyson’s needs and that the models are optimised and deployed within the project’s overall architecture.
The Associate Principal MLOps Engineer will also have responsibilities around architecting Machine Learning solutions using a mix of open-source and cloud tooling. You will also be involved in monitoring, troubleshooting, and resolving any issues that arise, and will be responsible for ensuring that our MLOps architecture is secure and compliant with data and cyber security regulations.
Previous experience in MLOps and machine learning is essential for this role, together with a strong track record of critical thinking in the software engineering domain and on pivoting software solutions based on continuous evolving business requirements.
- Build, deploy and manage MLOps infrastructure.
- Package, deploy and manage machine learning models.
- Build automated pipelines for the deployment and monitoring of machine learning models.
- Support data scientists with automation.
- Come up with concrete initiatives to automate and provide novel solutions to Data Science practitioners in Dyson (e.g. model monitoring, feature store, real-time interference, GenAI applications).
- Collaborate with other teams to ensure that models are integrated with the project’s overall architecture.
- Monitor, troubleshoot, and resolve issues that arise with the productionization of machine learning models.
- Analyse performance metrics to identify areas for improvement.
- Ensure that all models are secure and compliant with data and cyber security regulations.
- Degree in Computer Science or similar working experience.
- Proven track record of using high-quality Software Engineering standards.
- 7+ years working hands-on in the software engineering domain.
- 5+ years of experience productionizing Machine Learning models and/or building MLOps infrastructure solutions.
- Excellent skills using Python and building Python packages.
- Excellent skills using Git and version control principles.
- Excellent skills using container-based technologies (e.g. Docker).
- Excellent skills building CI/CD pipelines.
- Excellent skills using the command line and in Shell scripting.
- Excellent skills using Cloud Services, preferably using Google Cloud Platform services (e.g. GCE, GCS, AppEngine, CloudRun, IAM).
- Experience building APIs.
- Experience with the PyData stack.
- Experience with Terraform.
- Experience using Cloud MLOps technologies, SageMaker, Vertex AI or similar.
Good to Have
- Experience with Deep Learning frameworks like Tensorflow and PyTorch.
- Deep understanding of Machine Learning models.
- Experience on Cloud networking and data management services.
- Experience on building and releasing Python packages via PyPI or similar.
- Have presented a technical MLOps topic in a conference.
- Track record of open-source contributions to relevant technologies.
- Experience productionizing real-time pipelines.
- Track record on architecting complicated Machine Learning solutions.
- Ability to work independently and as part of a team.
- Passion for learning new technologies and techniques.
- Excellent communication to non-technical audience about technical topics.
- Excellent framing of technical jargon into precise and accurate descriptions to others.
- Has a passion to understand Dyson, to frame its problems, and to deliver tactical solutions in short timeframes when required.
Dyson is an equal opportunity employer. We know that great minds don’t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.