Hybrid opportunity at
PointClickCareSenior Machine Learning Systems Engineer (CAD)
The engineer sits on the team that owns the company's machine learning platform as its product. The group serves traditional ML and mixed ML/LLM work for…
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About This Role
The engineer sits on the team that owns the company's machine learning platform as its product. The group serves traditional ML and mixed ML/LLM work for engineering teams across the company, partnering closely with others to ship safe, scalable AI products. The role is hybrid and full time. The title carries a CAD marker, and the record does not give a specific office location. The engineer designs and runs the…
Job Description
The engineer sits on the team that owns the company's machine learning platform as its product. The group serves traditional ML and mixed ML/LLM work for engineering teams across the company, partnering closely with others to ship safe, scalable AI products. The role is hybrid and full time. The title carries a CAD marker, and the record does not give a specific office location.
The engineer designs and runs the platform that lets teams across the company build, deploy and scale ML solutions. Responsibilities include pipelines and tooling for training, evaluating, deploying, serving and monitoring models. Building MLOps tooling is also part of the work: model delivery pipelines, a model catalog, shared feature stores and experiment logs.
Security is another main responsibility. Security is built in, with authentication, role-based access, audit trails and compliance checks, and the platform is linked safely to existing systems and data sources while infrastructure is tuned for cost and scale. Mentoring engineers and promoting reusable platform patterns is expected.
The posting requires expert-level Python and Java, along with strong software engineering basics. Candidates should have built ML platforms and MLOps workflows using tools such as MLflow, Kubeflow or Ray, and should bring cloud experience, mainly on Azure. Docker and Kubernetes knowledge is listed. A degree in computing, machine learning or a neighboring field is preferred.
Responsibilities
- Turn machine learning requirements from product and engineering teams into reusable platform features
- Create dependable data and ML pipelines for training, evaluation, deployment and serving
- Build MLOps tooling for model delivery, a model catalog, shared feature stores and experiment logs
- Keep production ML systems observable and healthy, with alerts and automated fixes
- Build authentication, role-based access, audit logging and compliance monitoring into the platform
- Connect the platform securely to APIs and data sources, and optimize cost, speed and scale
- Mentor engineers and promote reusable platform patterns
Requirements
- Expert-level proficiency in Python and Java
- Strong software engineering fundamentals
- Hands-on history creating ML platforms and MLOps processes
- Familiarity with MLflow, Kubeflow, Ray and model-serving frameworks
- Cloud background, with Azure as the main platform and AWS or GCP as secondary options
- Experience with containers and orchestration using Docker and Kubernetes
Qualifications
- A university degree at bachelor's level or above, in computing, machine learning or a neighboring area
- Familiarity with Azure Machine Learning and Databricks processing environments
- Experience with security across large systems, such as role-based access, multi-factor authentication and network controls
- Experience speeding up and reducing the cost of heavy model training and inference, covering LLM serving
Core Skills
Benefits
- Benefits begin on the first day of employment
- Retirement plan with employer matching
- Flexible paid time off
- Wellness resources and support programs
- Parental and caregiver leave
- Fertility and adoption assistance
- Learning and development support
- Employee assistance program
- Inclusion and allyship groups
- Employee recognition
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Is this ML systems role remote or hybrid?
The listing describes the role as hybrid.
Which languages and tools are required?
The posting requires expert-level Python and Java, and asks for experience with MLflow, Kubeflow, Ray, Docker and Kubernetes.
Which cloud platform matters most?
Azure is named as the primary cloud platform, with AWS and GCP as secondary.
Is a salary listed?
No. The posting does not state a salary.
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