MLOps / Cloud Deployment Engineer

MLOps / Cloud Deployment Engineer (AI Applications) 
On-site | Vancouver Office – 675 W Hastings St. 

 

About EviSmart™ 

EviSmart™ is transforming modern dentistry with AI-powered CAD generation and mesh editing solutions. Trusted by dental labs and clinics in over 26 countries, our mission is to make dental care smarter, faster, and more efficient through advanced technology that connects dentists, labs, and manufacturers worldwide. 

About the Role 

We are looking for an MLOps / Cloud Deployment Engineer to deploy, scale, and optimize our AI-powered platform for cloud inference. You’ll own the cloud integration process — from containerizing AI modules to configuring GPU-enabled inference servers — ensuring the system can serve multiple users efficiently and cost-effectively. This role blends engineering expertise with cloud architecture and operational best practices. 

What You’ll Do 

• Containerize FastAPI backend and AI modules to create reproducible environments for inference and preprocessing pipelines. 

• Set up CI/CD workflows (GitHub Actions, GitLab CI, or similar) for seamless deployment. 

• Deploy backend services to cloud platforms (AWS, GCP, or Azure) using Cloud Run, ECS Fargate, Kubernetes, or similar technologies. 

• Configure autoscaling, load balancing, and secure API endpoints for robust, scalable performance. 

• Manage GPU-enabled instances for efficient AI inference. 

• Integrate object storage (S3 / GCS) and databases (PostgreSQL / MongoDB) for model weights, user uploads, and job tracking. 

• Implement asynchronous processing pipelines (Celery / Redis / PubSub) to optimize GPU utilization. 

• Monitor runtime performance, logging, and uptime using tools like Prometheus or Grafana. 

• Analyze cloud costs and performance, recommending optimizations and long-term architecture improvements. 

• Ensure secure data handling, HTTPS configuration, and compliance with privacy best practices. 

What We’re Looking For 

• Strong experience with cloud platforms (AWS, GCP, or Azure) and GPU-based workloads (PyTorch, TensorFlow, CUDA). 

• Proficiency with Docker, FastAPI, and Linux-based deployment. 

• Experience with CI/CD pipelines and infrastructure-as-code (Terraform, CloudFormation). 

• Knowledge of scalable architecture patterns (microservices, queues, autoscaling). 

• Competence with monitoring tools (Prometheus, Grafana, or equivalent). 

Nice-to-Have 

• Experience with MLOps frameworks such as Kubeflow, MLflow, or Vertex AI. 

• Understanding of 3D data or computational geometry for collaboration with AI teams. 

• Familiarity with frontend-backend integration, CORS, and API gateways. 

• Strong interest in optimizing AI systems for latency, cost efficiency, and user experience. 

Expected Deliverables (Initial Phase)

• Containerized version of the entire system (FastAPI + model inference).

• Cloud deployment with HTTPS endpoint accessible for UI integration.

• Configured storage and database backends.

• Auto-scaling and monitoring setup.

• Documentation for maintenance and future scaling.

 

Why Join Us? 

• Impactful Work: Deploy AI systems that improve dental workflows globally. 

• Collaborative Team: Work alongside engineers and AI experts in an innovative, problem-solving environment. 

• Growth Opportunities: Contribute to architecture decisions and help shape the future of our platform. 

• Perks That Matter: Competitive salary, benefits, and opportunities for professional development. 

Excited to optimize AI systems for real-world impact? Apply now and share your experience deploying and scaling AI platforms in the cloud.