Key Responsibilities

- Design and implement scalable MLOps platforms and machine learning deployment pipelines.
- Build and maintain CI/CD pipelines for machine learning models and applications.
- Automate model training, validation, deployment, monitoring, and retraining workflows.
- Deploy machine learning models to cloud and on-premises production environments.
- Develop automated ML pipelines using tools such as MLflow, Kubeflow, Airflow, or equivalent platforms.
- Containerize ML applications and services using Docker.
- Deploy and manage ML workloads using Kubernetes and container orchestration platforms.
- Implement model versioning, experiment tracking, model registry, and artifact management.
- Monitor model performance, data quality, system health, latency, availability, and resource utilization.
- Implement automated model retraining and continuous machine learning workflows.
- Develop infrastructure using Infrastructure as Code tools such as Terraform or CloudFormation.
- Integrate ML platforms with AWS, Microsoft Azure, or Google Cloud Platform.
- Build APIs and model-serving infrastructure using REST APIs, FastAPI, or similar technologies.
- Collaborate with Data Scientists to productionize machine learning and deep learning models.
- Implement security, access control, secrets management, and compliance for ML environments.
- Troubleshoot production ML pipelines, infrastructure,



deployment, and performance issues.
- Optimize cloud infrastructure and compute resources for cost, scalability, and performance.
- Establish observability and monitoring using tools such as Prometheus, Grafana, CloudWatch, or Azure Monitor.
- Implement automated testing for ML pipelines, data workflows, and model deployments.
- Maintain technical documentation, architecture diagrams, deployment procedures, and operational runbooks.

Technical Skills

Programming & Data

- Python
- SQL
- Bash/Shell scripting
- Pandas
- NumPy

Machine Learning & MLOps

- MLflow
- Kubeflow
- TensorFlow
- PyTorch
- Scikit-learn
- Model Registry
- Model Versioning
- Feature Engineering
- Model Monitoring
- Model Serving

Cloud Platforms

- AWS
- Microsoft Azure
- Google Cloud Platform (GCP)
- Amazon SageMaker
- Azure Machine Learning
- Google Vertex AI

DevOps & Infrastructure

- Docker
- Kubernetes
- Helm
- Terraform
- Ansible
- Git
- GitHub/GitLab
- Jenkins
- GitHub Actions
- Azure DevOps

Data & Workflow Platforms

- Apache Airflow
- Apache Spark
- Databricks
- Snowflake
- Kafka

Monitoring & Observability

- Prometheus
- Grafana
- ELK Stack
- CloudWatch
- Azure Monitor
- Application Insights

API & Model Deployment

- REST APIs
- FastAPI
- Flask
- Model Serving
- Microservices
- API Gateways

📌 MLOps Engineer (Canada)
🏢 Spait Infotech
📍 Canada