GFT TECHNOLOGIES IS A GLOBAL DIGITAL TRANSFORMATION COMPANY WITH OVER 12,000 SOLUTION-ORIENTED TECHNOLOGY PROFESSIONALS ACROSS 22 COUNTRIES. AS A DIGITAL TRANSFORMATION LEADER WITH A PASSION FOR INNOVATION, WE LEVERAGE NEXT-GENERATION TECHNOLOGIES TO RAISE OUR CLIENTS’ PRODUCTIVITY WITH INTELLIGENT SOFTWARE SOLUTIONS FOCUSED ON ENTERPRISE AI AND DATA, NEXTGEN FINANCE AND PLATFORM MODERNISATION. OUR STRENGTH IS GROUNDED IN DEEP TECHNOLOGICAL EXPERTISE, A STRONG ECOSYSTEM OF PARTNERS, AND PROFOUND INDUSTRY KNOWLEDGE ACROSS BANKING, INSURANCE AND MANUFACTURING AND AUTOMOTIVE. IN CANADA, WE HAVE OFFICES IN TORONTO, QUEBEC & MONTREAL. BE A PART OF OUR WONDERFUL TEAM AND “LET’S GO BEYOND !”

We are currently seeking an experienced professional to join our team in the role of Machine Learning Architect – Contract

Here some Key Responsibilities:

• Design, develop, and maintain efficient MLOps solutions for continuous integration and continuous deployment (CI/CD) of machine learning models.
• Collaborate with data science teams to understand model requirements and translate them into robust MLOps solutions.
• Implement tools and pipelines to automate the model lifecycle, including data preprocessing, training, testing, deployment, and monitoring in production.
• Manage the scaling of machine learning models for production environments, ensuring performance and scalability.
• Implement version control practices and model tracking to ensure traceability, reproducibility, and governance.
• Work with cloud infrastructure (AWS) and containerization tools (Docker, Kubernetes).
• Ensure continuous monitoring of models in production to maintain accuracy, detect drifts, and recommend adjustments.
• Collaborate with security teams to ensure compliance with data and model security standards and best practices.
• Participate in the evaluation and integration of new technologies and best practices in the MLOps ecosystem.

REQUIRED SKILLS AND QUALIFICATIONS:

• 5-7 years of experience in MLOps, Data Science, or DevOps, with expertise in deploying ML models to production environments.
• Proficiency in version control tools (Git, DVC) and deployment frameworks
• Strong experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI for automating ML workflows.
• Deep knowledge of cloud infrastructure management (AWS) and container orchestration tools (Docker, Kubernetes).
• Experience with model management systems (MLflow, Kubeflow, etc.).
• Experience with databases and handling large-scale data processing.
• Excellent communication and collaboration skills, both technical and non-technical.
• Proficiency in English (written and spoken)

ABOUT US

We show commitment to our investors and stand for solid, long-term growth performance. Founded in Germany in 1987 and in American territory since 2008, GFT expanded globally to over 10,000 experts. And to more than 15 markets to ensure proximity to clients. With new opportunities from Asia to Brazil, the international growth story continues. We are committed to grow tech talents worldwide. Because our team’s strong consulting and development skills across legacy and pioneering technologies, like GreenCoding, underpin success. We maintain a family atmosphere in an inclusive work environment.
At GFT we are committed to cultivating, fostering, and preserving a culture of diversity, equity, and inclusion. We want to attract, recruit, develop and retain the most talented employees, regardless of their background. We are guided by our core values in everything we do, and recognize that being a diverse and inclusive employer helps us fulfil our social responsibility to make a difference.
At GFT, #MakeYourMark implies a commitment to always put you first, while helping you push your boundaries to achieve greatness, and giving you the freedom to innovate and create beyond conventions.

Here some Key Responsibilities:

• Design, develop, and maintain efficient MLOps solutions for continuous integration and continuous deployment (CI/CD) of machine learning models.
• Collaborate with data science teams to understand model requirements and translate them into robust MLOps solutions.
• Implement tools and pipelines to automate the model lifecycle, including data preprocessing, training, testing, deployment, and monitoring in production.
• Manage the scaling of machine learning models for production environments, ensuring performance and scalability.
• Implement version control practices and model tracking to ensure traceability, reproducibility, and governance.
• Work with cloud infrastructure (AWS) and containerization tools (Docker, Kubernetes).
• Ensure continuous monitoring of models in production to maintain accuracy, detect drifts, and recommend adjustments.
• Collaborate with security teams to ensure compliance with data and model security standards and best practices.
• Participate in the evaluation and integration of new technologies and best practices in the MLOps ecosystem