AI/ML & DevOps EngineerL
Role summary
We are seeking a motivated AI/ML & DevOps Engineer with over 2 years of experience to bridge the gap between machine learning models and production systems. This role involves designing, developing, and deploying ML models using Python and relevant frameworks, managing the end-to-end ML lifecycle, and building robust MLOps pipelines. Responsibilities include developing data pipelines, containerizing applications with Docker, managing Kubernetes deployments, implementing CI/CD, and utilizing Infrastructure as Code tools. The engineer will also set up monitoring and logging, and work with cloud platforms like AWS, GCP, and Azure. This full-time, permanent position offers an opportunity to work on shaping production-ready AI systems in a fast-growing, innovative team.
About the Role
We are looking for a motivated engineer with 2+ years of experience in AI/ML/Data Science and DevOps to join our team. The ideal candidate is someone who can bridge the gap between machine learning and production systems—building robust models, ensuring smooth deployment, and maintaining scalable infrastructure.
Key Responsibilities
Design, develop, and experiment with ML models using Python (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow).
Manage the end-to-end ML lifecycle: preprocessing, feature engineering, training, evaluation, deployment, and monitoring.
Build and maintain MLOps pipelines including CI/CD, reproducibility, and model versioning.
Develop and maintain data pipelines & ETL workflows (Airflow, Prefect, or similar).
Create effective data visualizations using Matplotlib, Seaborn, Plotly, or BI tools.
Deploy ML workloads on cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
Administer Linux servers (Ubuntu/CentOS) and write shell scripts for automation.
Containerize applications with Docker and manage deployments using Kubernetes (basics required).
Implement CI/CD workflows with tools such as GitHub Actions, GitLab CI, or Jenkins.
Use Infrastructure as Code (IaC) tools like Terraform or Ansible to provision infrastructure.
Set up monitoring & logging with Prometheus, Grafana, ELK/EFK stack.
Understand and troubleshoot basic networking concepts (DNS, load balancers, SSL/TLS, firewalls).
Work with cloud platforms (AWS/GCP/Azure) for infrastructure provisioning and workload deployment.
Qualifications
2+ years of hands-on experience in AI/ML and/or DevOps.
Strong programming skills in Python for data science and ML.
Experience with ML frameworks and DevOps tools listed above.
Strong problem-solving mindset and ability to work in cross-functional teams.
Awareness of best practices in software engineering, version control, and testing.
Nice to Have
Exposure to distributed training of ML models.
Contributions to open-source ML/DevOps projects.
Experience in high-scale production systems.
Why Join Us?
Opportunity to work at the intersection of ML and DevOps.
Be part of a fast-growing, innovative team shaping production-ready AI systems.
Growth-focused environment with learning opportunities, ownership, and impact from day one.
Job Types: Full-time, Permanent
Pay: $50,000.00-$100,000.00 per year
Ability to commute/relocate:
- Mississauga, ON: reliably commute or plan to relocate before starting work (required)
Work Location: In person