Top Benefits
About the role
CanCap Group Inc. is part of privately-owned Canadian national financial services company with multiple verticals across automotive, consumer, and merchant lending portfolios. We manage the entire lifecycle of the finance receivable from credit adjudication through to contract administration, customer service, default management and post charge-off recoveries. We are a company of innovators, we learn from each other, respect each other and create together. When it comes to our customers, partners, and each other, we are always motivated by doing the “right thing”. We are always looking to find the best people and the right methods that allow us to meet this goal and look to the future for growth.
What Your Day and Week Could Look Like
Reporting to the Head of Data Platform, the Machine Learning Engineer will be responsible for designing & deploying ML models and MLOps infrastructure used by the Data Platform teams.
We are seeking a highly skilled and experienced Machine Learning Engineer with a strong background in Generative AI to join our AI/ML team. You will be responsible for developing, deploying, and optimizing state-of-the-art machine learning models, with a focus on generative techniques including LLMs, diffusion models and deep learning frameworks. This role is ideal for someone passionate about solving complex problems using cutting-edge AI research and scalable ML infrastructure.
Key Responsibilities
- Design, implement, and optimize Generative AI models (e.g., LLMs, VAEs, GANs, Diffusion Models).
- Collaborate with data scientists, product teams, engineers and researchers to integrate ML models into real-world applications.
- Develop scalable and maintainable ML pipelines using modern tools and cloud infrastructure (AWS, GCP, or Azure).
- Fine-tune and customize pre-trained models (e.g., GPT, BERT, LLaMA, Stable Diffusion) for domain-specific tasks.
- Conduct research and stay up to date with the latest advancements in generative modeling, deep learning, and transformer architectures.
- Ensure model interpretability, fairness, and robustness through rigorous evaluation and testing.
- Contribute to code reviews, design discussions, and technical mentoring of junior team members.
- Evaluate emerging technologies and tools to continuously improve the MLOps landscape.
What You Bring
- 4+ years of industry experience in Machine Learning, with at least 2 years in Generative AI.
- Advanced proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or JAX.
- Deep understanding of neural networks, natural language processing (NLP), and computer vision.
- Experience in building ML pipelines supporting databricks workflows.
- Hands-on experience in architecting low-code AI solutions, scaling prototypes into production grade ML Models with ML pipeline automation & orchestration.
- Experience with transformers, autoencoders, and/or diffusion-based models.
- Strong software engineering practices (version control, testing, CI/CD).
- Familiarity with MLOps tools (e.g., MLflow, Kubeflow, Airflow) and deployment on cloud platforms.
- Excellent communication and stakeholder management skills.
Preferred Qualifications
- GCP certification (e.g., Professional Machine Learning Engineer)
- Experience with RLHF, prompt engineering, or multi-modal models.
- Contributions to open-source ML/AI projects or research publications.
- Experience in deploying models at scale in production environments.
- Knowledge of vector databases, retrieval-augmented generation (RAG), or embedding-based systems.
- Experience working in Agile and DevOps environments.
- Excellent communication and stakeholder management skills.
Nice to Have
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field (Ph.D. preferred for research-focused roles).
- Exposure to cloud native software development best practices.
What You Can Expect From Us
Our Employee Experience is aimed at supporting and inspiring our talented team through:
- A passionate team dedicated to supporting and empowering others.
- An environment where creative, innovative thinking is encouraged.
- Health and Dental Benefits.
Work Location & Remote Flexibility
- This role follows a hybrid model, requiring employees to work 50% in-office, with flexibility to work remotely or from the office on other days.
- The company has two office locations:
- Downtown Toronto (Church Street) – The tech team is primarily based here.
- Mississauga – Another office location, but less frequently used by the tech team.
CanCap is an equal opportunity employer and values diversity. We are committed to building and evolving a team reflecting a variety of backgrounds, perspectives, and skills. To be considered for employment, you will need to successfully pass a criminal background check and validate your work experience.
Next Steps
Adding to our team is an important step in our business. We’ve taken time to be purposeful and thoughtful with this job posting, and we encourage you to do the same with your application. Help us understand how your experience aligns with this role and how you can contribute to our Databricks-driven data platform.
About CanCap Group Inc.
We manage the entire lifecycle of the finance receivable from credit adjudication through to contract administration, customer service, default management and post charge-off recoveries. We are a company of innovators: we learn from each other, respect each other, and create together. We strive to inspire our customers by continually understanding them, meeting their needs, and keeping them happily surprised. And we always do so with integrity.
Nous gérons tout un cycle de vie de la créance financière, de l'adjudication de crédit à l'administration des contrats, au service à la clientèle, à la gestion des défauts et aux recouvrements après imputation. Nous sommes une entreprise d'innovateurs: nous apprenons mutuellement, nous nous respectons et créons ensemble. Nous nous efforçons d'inspirer nos clients en les écoutant, en répondant à leurs besoins et en les gardant agréablement surpris. Et nous le faisons toujours avec intégrité.
Top Benefits
About the role
CanCap Group Inc. is part of privately-owned Canadian national financial services company with multiple verticals across automotive, consumer, and merchant lending portfolios. We manage the entire lifecycle of the finance receivable from credit adjudication through to contract administration, customer service, default management and post charge-off recoveries. We are a company of innovators, we learn from each other, respect each other and create together. When it comes to our customers, partners, and each other, we are always motivated by doing the “right thing”. We are always looking to find the best people and the right methods that allow us to meet this goal and look to the future for growth.
What Your Day and Week Could Look Like
Reporting to the Head of Data Platform, the Machine Learning Engineer will be responsible for designing & deploying ML models and MLOps infrastructure used by the Data Platform teams.
We are seeking a highly skilled and experienced Machine Learning Engineer with a strong background in Generative AI to join our AI/ML team. You will be responsible for developing, deploying, and optimizing state-of-the-art machine learning models, with a focus on generative techniques including LLMs, diffusion models and deep learning frameworks. This role is ideal for someone passionate about solving complex problems using cutting-edge AI research and scalable ML infrastructure.
Key Responsibilities
- Design, implement, and optimize Generative AI models (e.g., LLMs, VAEs, GANs, Diffusion Models).
- Collaborate with data scientists, product teams, engineers and researchers to integrate ML models into real-world applications.
- Develop scalable and maintainable ML pipelines using modern tools and cloud infrastructure (AWS, GCP, or Azure).
- Fine-tune and customize pre-trained models (e.g., GPT, BERT, LLaMA, Stable Diffusion) for domain-specific tasks.
- Conduct research and stay up to date with the latest advancements in generative modeling, deep learning, and transformer architectures.
- Ensure model interpretability, fairness, and robustness through rigorous evaluation and testing.
- Contribute to code reviews, design discussions, and technical mentoring of junior team members.
- Evaluate emerging technologies and tools to continuously improve the MLOps landscape.
What You Bring
- 4+ years of industry experience in Machine Learning, with at least 2 years in Generative AI.
- Advanced proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or JAX.
- Deep understanding of neural networks, natural language processing (NLP), and computer vision.
- Experience in building ML pipelines supporting databricks workflows.
- Hands-on experience in architecting low-code AI solutions, scaling prototypes into production grade ML Models with ML pipeline automation & orchestration.
- Experience with transformers, autoencoders, and/or diffusion-based models.
- Strong software engineering practices (version control, testing, CI/CD).
- Familiarity with MLOps tools (e.g., MLflow, Kubeflow, Airflow) and deployment on cloud platforms.
- Excellent communication and stakeholder management skills.
Preferred Qualifications
- GCP certification (e.g., Professional Machine Learning Engineer)
- Experience with RLHF, prompt engineering, or multi-modal models.
- Contributions to open-source ML/AI projects or research publications.
- Experience in deploying models at scale in production environments.
- Knowledge of vector databases, retrieval-augmented generation (RAG), or embedding-based systems.
- Experience working in Agile and DevOps environments.
- Excellent communication and stakeholder management skills.
Nice to Have
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field (Ph.D. preferred for research-focused roles).
- Exposure to cloud native software development best practices.
What You Can Expect From Us
Our Employee Experience is aimed at supporting and inspiring our talented team through:
- A passionate team dedicated to supporting and empowering others.
- An environment where creative, innovative thinking is encouraged.
- Health and Dental Benefits.
Work Location & Remote Flexibility
- This role follows a hybrid model, requiring employees to work 50% in-office, with flexibility to work remotely or from the office on other days.
- The company has two office locations:
- Downtown Toronto (Church Street) – The tech team is primarily based here.
- Mississauga – Another office location, but less frequently used by the tech team.
CanCap is an equal opportunity employer and values diversity. We are committed to building and evolving a team reflecting a variety of backgrounds, perspectives, and skills. To be considered for employment, you will need to successfully pass a criminal background check and validate your work experience.
Next Steps
Adding to our team is an important step in our business. We’ve taken time to be purposeful and thoughtful with this job posting, and we encourage you to do the same with your application. Help us understand how your experience aligns with this role and how you can contribute to our Databricks-driven data platform.
About CanCap Group Inc.
We manage the entire lifecycle of the finance receivable from credit adjudication through to contract administration, customer service, default management and post charge-off recoveries. We are a company of innovators: we learn from each other, respect each other, and create together. We strive to inspire our customers by continually understanding them, meeting their needs, and keeping them happily surprised. And we always do so with integrity.
Nous gérons tout un cycle de vie de la créance financière, de l'adjudication de crédit à l'administration des contrats, au service à la clientèle, à la gestion des défauts et aux recouvrements après imputation. Nous sommes une entreprise d'innovateurs: nous apprenons mutuellement, nous nous respectons et créons ensemble. Nous nous efforçons d'inspirer nos clients en les écoutant, en répondant à leurs besoins et en les gardant agréablement surpris. Et nous le faisons toujours avec intégrité.