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Key Responsibilities 2. ML & Data Engineering 3. CI/CD for ML (MLOps) 4. Automation & Scripting 5. DevOps & Containerization 6. Monitoring & Observability 7. Security & Compliance 8. Data Science Fundamentals
- Cloud Platform Services (GCP)
- Hands-on experience with GCP services: Vertex AI, BigQuery, Cloud Storage, Cloud Functions, Cloud Pub/Sub, Cloud Build, Cloud Run, Airflow.
- Platform visibility with Element Platform tools (if applicable).
- Solid understanding of ML workflows and LLM concepts (training, validation, hyperparameter tuning).
- Hands-on with TensorFlow, Keras, PyTorch, scikit-learn.
- Build ETL pipelines using PySpark and Scala on Dataproc (serverless).
- CI/CD tools like Looper, Jenkins for ML pipeline automation.
- Experience in Vertex AI Pipelines, model versioning, and continuous deployment.
- Strong skills in Python, Bash, and SQL.
- Automate ML workflows and processes.
- Experience with Kubernetes (GKE) and Docker.
- Helm charts and YAML for Kubernetes deployment (good to have).
- Use tools like Cloud Monitoring, Cloud Logging, Prometheus, Grafana.
- Monitor model performance with Vertex AI Model Monitoring.
- Knowledge of VPC, firewall rules, service accounts, and Secret Manager.
- Understanding of data science lifecycle, methodologies, and best practices.
- GCP Services: Vertex AI, BigQuery, Cloud Functions, Pub/Sub, Cloud Run, Cloud Build, Airflow
- ML Frameworks: TensorFlow, Keras, PyTorch, scikit-learn
- Programming: Python, Bash, SQL
- CI/CD Tools: Looper, Jenkins
- MLOps Practices and Pipelines
Key Skills
Ranked by relevance
cloud
ai
cicd
kubernetes
tensorflow
pytorch
python
keras
mlops
bash
gcp
containerization
prometheus
firewall
jenkins
storage
devops
scala
etl
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- Posted
- Aug 26, 2025
- Type
- Contract
- Level
- Entry
- Location
- Mississauga
- Company
- J&M Group
Industries
IT Services
IT Consulting
Categories
Engineering
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