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Deep Learning Developer Jobs in Toronto, ON (NOW HIRING)

Senior Product Manager - AI

Toronto, ON · On-site

CA$94K - CA$176K/yr

Applies knowledge of large language models, machine learning, deep learning, retrieval-augmented generation, prompt engineering, model evaluation and data governance to guide delivery decisions.

New

Engineering, but brighter. About the Role As a Principal AI Engineer in Agent Factory, you'll ... Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms ...

Bachelor's degree in Computer Science, Software Engineering, or a related field. * Extensive experience using Python including a strong grasp on machine learning and deep learning toolkits (e.g.

AI Engineer, AidenSales RBC Capital Markets is seeking an AI Engineer with deep expertise in Generative AI, neural networks, and transfer learning to support Sales teams with cutting-edge AI-powered ...

... deep learning frameworks such as TensorFlow or PyTorch and optimizing performance on diverse ... Work closely with software and DevOps engineers to deploy GenAI models. * Document code, algorithms ...

... field engineering role. * Proficiency in Python, with hands-on experience training, fine-tuning, evaluating, and deploying deep learning models, including modern LLM architectures. * Experience ...

Machine Learning Engineer

Toronto, ON · On-site

$120 - $160/hr

What you'll get to do in this role We are seeking a highly capable Machine Learning Engineer with deep expertise in building Lang graph based agentic systems, Object‑Oriented Python, and ML Ops. In ...

... field engineering role. * Proficiency in Python, with hands-on experience training, fine-tuning, evaluating, and deploying deep learning models, including modern LLM architectures. * Experience ...

Showing results 41-60

Deep Learning Developer information

What are the key skills and qualifications needed to thrive as a deep learning developer?

To thrive as a Deep Learning Developer, you need a strong background in computer science, mathematics, and proficiency in programming languages like Python, often supported by a degree in a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, and experience with cloud platforms or GPU acceleration, are commonly required technical skills. Analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this role. These competencies are crucial for designing, training, and deploying advanced neural network models that address complex real-world problems.

What is a deep learning developer?

Deep Learning Developers are specialized software engineers or data scientists who design, build, and implement artificial intelligence systems using deep learning techniques. They work with neural networks, large datasets, and various frameworks like TensorFlow or PyTorch to develop models for tasks such as image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, optimization, and deployment to solve complex problems that require advanced pattern recognition. Deep Learning Developers often collaborate with AI researchers, data engineers, and product teams to integrate intelligent features into applications.

What is the difference between Deep Learning Developer vs Machine Learning Engineer?

AspectDeep Learning DeveloperMachine Learning Engineer
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with neural networksBachelor's or Master's in CS, Data Science, or related; knowledge of algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on neural networksData-driven companies, software firms, industries applying machine learning
Industry UsagePrimarily in AI research, neural network development, deep learning projectsBroader application including predictive modeling, data analysis, and ML systems

Deep Learning Developers specialize in neural networks and deep learning models, often working on AI research and complex algorithms. Machine Learning Engineers have a broader focus on developing, deploying, and maintaining machine learning models across various applications. While both roles require similar educational backgrounds, their focus areas and industry applications differ.

What are some common challenges deep learning developers face when deploying models to production environments?

Deep Learning Developers often encounter challenges such as optimizing model performance for real-time inference, managing resource constraints (like GPU/CPU availability), and ensuring model reproducibility across different environments. Additionally, integrating deep learning models into existing software systems and maintaining them over time can be complex, especially as data and requirements evolve. Collaborating closely with DevOps, data engineers, and QA teams is essential to address these challenges and ensure smooth deployment and ongoing reliability.
Infographic showing various Deep Learning Developer job openings in Toronto, ON as of August 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, 25% Hybrid, and 25% Remote job distribution.

Senior Product Manager - AI

BMO Capital Markets

Toronto, ON • On-site

CA$94K - CA$176K/yr

Full-time

Medical, Life, Retirement

Posted 2 days ago

New


Job description

Application Deadline:

08/20/2026

Address:

4100 Gordon Baker Road

Job Family Group:

Technology

The Senior Product Manager - AI will contribute to the success of the Service, Operations, and Support (SOS) organization, a technology department within T&O's Engineering and Platforms. The SOS organization is the face of technology to internal end-users, including system & network operations, IT Help Desk, end-user asset deployment, and IT Service Management (ITSM) governance practices. The following responsibilities apply to the Senior Product Manager - AI.

Leads the strategy, implementation and ongoing management of enterprise AI capabilities. Owns the end-to-end lifecycle from use case identification, feasibility assessment, product vision and roadmap through solution design, implementation, adoption, performance monitoring and continuous improvement. Partners with business, technology, data science, machine learning engineering, architecture, cybersecurity, risk, compliance and vendor teams to deliver scalable, secure, governed and measurable AI solutions that create business value. Additional responsibilities include:

  • Defines the vision, success measures, roadmap and delivery priorities for Gen AI systems aligned to business and technology objectives.
  • Assesses and prioritizes Gen AI use cases based on business value, technical feasibility, data readiness, implementation complexity, risk and adoption potential.
  • Translates business needs into requirements, epics, user stories, acceptance criteria and implementation plans for cross-functional delivery teams.
  • Leads solution design and implementation activities, including prompt workflows, knowledge retrieval, data pipelines, model integration, controls, testing and release readiness.
  • Applies knowledge of large language models, machine learning, deep learning, retrieval-augmented generation, prompt engineering, model evaluation and data governance to guide delivery decisions.
  • Ensures AI solutions meet enterprise expectations for responsible AI, privacy, security, data protection, auditability, accessibility, resilience and risk management.
  • Drives implementation readiness, adoption, change management, communications, training and transition to operational support.
  • Monitors post-implementation performance, including quality, accuracy, usefulness, adoption, reliability, model drift, user feedback, risk indicators and business outcomes.
  • Builds effective relationships across business, technology, governance and vendor teams; removes blockers and communicates complex AI concepts in clear business language.
  • Produces regular reporting and executive updates on roadmap progress, delivery risks, adoption, value realization, system health and continuous improvement opportunities.
  • Operates at a group or enterprise-wide level as a specialist resource to senior leaders and stakeholders.
  • Takes measured risks while protecting the bank by applying the Risk Management Framework, Risk Culture and approved Risk Appetite in alignment with policy documents, laws and regulations.

Qualifications:

  • Typically 7+ years of relevant experience and a post-secondary degree in technology, computer science, data science, engineering, business or a related field, or an equivalent combination of education and experience.
  • Seasoned professional with experience delivering technology, AI, or enterprise platform initiatives from concept through implementation and sustainment.
  • Proficient in product management, project delivery, technology business requirements, implementation planning and stakeholder management.
  • In-depth knowledge of ITSM and the ITIL methodology and its practices
  • Strong understanding of of Generative AI, machine learning, deep learning, large language models, prompt engineering, retrieval-augmented generation, model evaluation and AI solution design.
  • Strong knowledge of responsible AI, trust, bias, ethics, privacy, security, risk management, and operational sustainment.
  • Knowledgeable in data wrangling, preprocessing, governance, visualization, data-driven decision making, computational thinking, programming concepts, ML algorithms, and model scaling
  • Experience with ServiceNow platform is a strong asset
  • Expert analytical and problem-solving skills, including data-driven decision making
  • Expert communication, influence, and collaboration skills, with the ability to manage ambiguity and explain complex AI concepts to business and executive audiences
  • Expert verbal and written communication skills

Note: This role requires 2 days in the office.

Salary:

$94,600.00 - $176,000.00

Pay Type:

Salaried

The above represents BMO Financial Group's pay range and type.

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group's expected target for the first year in this position.

BMO Financial Group's total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit:https://jobs.bmo.com/global/en/Total-Rewards

About Us

At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world.

As a member of the BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one - for yourself and our customers. We'll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we'll help you gain valuable experience, and broaden your skillset.

To find out more visit us at https://jobs.bmo.com/ca/en.

BMO is committed to an inclusive, equitable and accessible workplace. By learning from each other's differences, we gain strength through our people and our perspectives. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation, please contact your recruiter.

Note to Recruiters: BMO does not accept unsolicited resumes from any source other than directly from a candidate. Any unsolicited resumes sent to BMO, directly or indirectly, will be considered BMO property. BMO will not pay a fee for any placement resulting from the receipt of an unsolicited resume. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.