Jobs / MDA
Intermediate DevOps Engineer
MDA · Richmond, BC, Canada
Richmond, BC, CanadaExp: 3+ yrs78,000-108,000 CAD/yearlyHybrid
Remuneration
Competitive health care, dental and vision benefits, disability income protection, life insurance, group retirement savings plans, and employee and family assistance program
Location
Richmond, BC, Canada
Visa sponsorship
Not specified
Job summary
The DevOps Specialist builds and maintains infrastructure and tooling that enable machine learning teams to develop, deploy, monitor, and improve models at scale. The role designs a secure MLOps platform using Kubernetes, Terraform, and AWS; automates model lifecycle pipelines; and implements observability, security, and compliance controls for geointelligence AI and machine learning systems. It also partners with analysts to turn research notebooks into production-ready pipelines.
Benefits
Extended health care and flexible drug plansDental and vision benefitsDisability income protectionLife insuranceGroup retirement savings plansEmployee and family assistance program
Qualifications
- Degree in Computer Science, Engineering, Data Science, or a related field.
- At least 3 years of software development experience, including at least 2 years focused on MLOps.
- Proficiency in Python and at least one compiled language: C, C++, Go, or Rust.
- Experience with Docker and Kubernetes.
- Experience with Terraform or Ansible and CI/CD/CT tooling.
- Knowledge of model serving frameworks, experiment and metadata tracking, and monitoring and observability.
- Experience with AWS compute, storage, and AI/ML services, including EKS, SageMaker, or Bedrock.
- Ability to explain technical concepts to non-technical stakeholders.
- Proactive problem-solving skills and comfort working in a fast-paced, mission-critical environment.
- Ability to obtain and maintain reliability status security clearance and pass a Controlled Goods Program security assessment.
Responsibilities
- Design and evolve a secure, scalable MLOps platform with model registry, CI/CD/CT, feature store, experiment tracking, and monitoring.
- Build reusable infrastructure-as-code modules for data ingestion, model serving, and autoscaling.
- Automate pipelines that move model code, data schemas, and container images into production with minimal downtime.
- Integrate model versioning with data lineage and metadata tracking.
- Monitor live models for latency, throughput, drift, and resource utilization.
- Develop alerts and self-healing mechanisms for model anomalies and hardware failures.
- Help machine learning analysts turn research notebooks into production pipelines through tooling, guidance, and code reviews.
- Collaborate with IT, governance, and platform teams on MLOps practices and platform adoption.
- Implement encryption, role-based access control, and secure secret management.
- Support risk assessments and audits for geointelligence AI and machine learning systems.
- Evaluate emerging technologies and contribute to reusable open-source components and internal libraries.
Skills
AnsibleAWSC++DockerEKSGoKubernetesPythonRustTerraform
Degrees
Computer ScienceEngineeringData ScienceRelated field
Languages
PythonCC++GoRust
Industry
GeointelligenceSpace
Security clearance
Reliability statusControlled Goods Program security assessment
Relocation
No