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Generative Ai Testing Jobs in Ontario (NOW HIRING)

Royal Bank of Canada

Staff AI Engineer

Toronto, ON · On-site

... Testing, Generative AI, Mentorship, Model Context Protocol, Production Software, Software Systems, Vector Databases Additional Job Details Address: RBC CENTRE, 155 WELLINGTON ST W:TORONTO City:

iManage

Senior AI Engineer

Toronto, ON · On-site

... our generative AI document assistant, as well as document classification, extraction, and LLM ... testing built in * Applying modern engineering practices for production AI systems, including ...

Deloitte

AI Solutions Architect

Toronto, ON

Generative AI Architecture • Design and deliver end-to-end GenAI solutions from concept through ... Testing, monitoring, evaluation frameworks • Establish patterns for prompt management, model ...

Vangard, Inc.

Senior AI Scientist

Toronto, ON · On-site

Design and execute adversarial evaluations of generative AI and agentic systems, probing for safety ... testing, and the ability to identify confounds and methodological flaws in existing analyses

Td

Applied Machine Learning Scientist II

Toronto, ON

CA$125K - CA$154K/yr

Experience developing, evaluating, and deploying machine learning and Generative AI solutions. Strong understanding of model evaluation methodologies, experimentation, statistical testing, and ...

Manulife

Senior Data Scientist

Toronto, ON · Hybrid

Stay up-to-date with the latest advancements in Generative AI, prompt engineering, Agentic ... build processes, testing, and operations. * Strong collaboration and elaboration skills ...

Manulife

Lead Data AI Architect

Toronto, ON · Hybrid

... testing, monitoring, and cost controls). * Ensure interoperability, data consistency, and ... Experience with Generative AI solution patterns (e.g., RAG, vector databases/search, prompt ...

Ag

Sr AI/ML Software Developer

Waterloo, ON · Hybrid

Establish MLOps best practices and mentor team members on model deployment, A/B testing, drift ... and Generative AI platforms from other cloud providers for model deployment and serving.

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Generative Ai Testing information

What is the difference between Generative Ai Testing vs Data Scientist?

AspectGenerative Ai TestingData Scientist
Required CredentialsKnowledge of AI models, testing tools, programming skillsStatistics, programming, data analysis certifications
Work EnvironmentAI development teams, testing labs, tech companiesResearch labs, tech firms, finance, healthcare
Employer & Industry UsageAI product testing, quality assurance in techData analysis, predictive modeling across industries

Generative Ai Testing focuses on evaluating and validating AI-generated content and models, ensuring quality and accuracy. Data Scientists analyze data, build models, and derive insights. While both roles require programming and AI knowledge, Generative Ai Testing emphasizes testing processes, whereas Data Scientists focus on data analysis and model development.

How do I become a Generative AI Testing?

To become a Generative AI Tester, develop skills in machine learning, natural language processing, and programming languages like Python. Gain experience with AI frameworks such as TensorFlow or PyTorch and understand data quality and model evaluation techniques. Certifications in AI or data science can enhance your qualifications and improve job prospects.

Is Generative AI Testing a good career?

Generative AI Testing is a growing field within AI development that involves evaluating the quality and safety of AI-generated content. It requires skills in machine learning, programming, and understanding AI models, making it a promising career path with increasing demand as AI technologies expand. Professionals in this area can find opportunities in tech companies, research labs, and startups focused on AI innovation.

What are the key skills and qualifications needed to thrive as a generative AI testing specialist, and why are they important?

To thrive as a Generative AI Testing Specialist, you need a robust understanding of machine learning principles, model evaluation techniques, and a background in computer science or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and model evaluation frameworks, as well as experience with automated testing platforms, is typically required. Analytical thinking, attention to detail, and strong communication skills help you identify model weaknesses and collaborate effectively with development teams. These skills are crucial to ensure the reliability, safety, and ethical deployment of generative AI solutions.

What are some common challenges faced when testing generative AI models, and how can I prepare to address them in this role?

Testing generative AI models often involves unique challenges such as evaluating the quality and relevance of generated content, detecting bias or inappropriate outputs, and ensuring model consistency across various prompts. You may work closely with data scientists and engineers to create robust evaluation frameworks and develop automated as well as manual testing strategies. Familiarity with prompt engineering, statistical evaluation techniques, and domain-specific knowledge will help you address these challenges effectively. Proactively staying updated on industry best practices and collaborating with cross-functional teams are key to success in this dynamic field.

What is generative AI testing?

Generative AI Testing refers to the process of evaluating and validating AI systems, particularly those that generate content such as text, images, or code. This type of testing focuses on assessing the accuracy, reliability, fairness, and safety of generative models to ensure they function as intended and avoid producing harmful or biased outputs. Testers use various methods, including automated and manual techniques, to check for issues like hallucinations, inappropriate content, or security vulnerabilities. The goal is to build trust in generative AI systems and ensure they meet quality and ethical standards before deployment.
What job categories do people searching Generative Ai Testing jobs in Ontario look for? The top searched job categories for Generative Ai Testing jobs in Ontario are:
What cities in Ontario are hiring for Generative Ai Testing jobs? Cities in Ontario with the most Generative Ai Testing job openings:
Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in Ontario as of August 2026, with employment types broken down into 94% Full Time, 3% Part Time, and 3% Contract. Highlights an 61% In-person, 21% Hybrid, and 18% Remote job distribution.

Staff AI Engineer

Royal Bank of Canada

Toronto, ON • On-site

Full-time

Re-posted 23 days ago

Job description

Job Description

What is the Opportunity?

Global Functions Technology (GFT) is part of RBC's Technology and Operations division. GFT's impact is far-reaching as we collaborate with partners from across the company to deliver innovative and transformative IT solutions. Our clients represent Risk, Finance, HR, CAO, Audit, Legal, Compliance, Financial Crime, Capital Markets, Personal and Commercial Banking and Wealth Management. We also lead the development of digital tools and platforms to enhance collaboration.

As aStaff Engineeron theASEDA team, you'll be at the center of RBC's most ambitious AI bet - building the agentic AI and generative AI systems that are transforming how the enterprise operates. Think AI agents that can reason, plan, use tools, and take action across real business workflows - not chatbot demos, but production systems that move the needle.

You'll design and ship MCP servers, agent orchestration pipelines, and RAG systems that connect large language models to the enterprise. You'll work across teams, set technical direction, and build the reusable patterns that let others move faster. This is a role where your code ships, your architecture decisions stick, and your ideas shape how AI gets adopted at scale.

We value positive attitude, willingness to learn, open communication, teamwork, and commitment to clean, secure and well-tested code.

What Will You Do?

  • Build agentic AI systems end-to-end- design and implement agent orchestration, MCP servers, RAG pipelines, and multi-agent workflows that run reliably in production
  • Set technical direction- make architecture decisions on how agents interact with tools, manage context, route between models, and recover from failures
  • Ship production AI- write clean, well-tested Python code for systems that real users and business teams depend on every day
  • Drive quality and safety- build evaluation frameworks, prompt versioning, guardrails, and observability so agent behavior stays reliable across model updates
  • Enable other teams- create reusable components, run enablement sessions, and mentor engineers on agentic AI patterns and best practices
  • Stay at the frontier- evaluate emerging models, frameworks, and protocols (MCP, A2A) and bring the best ideas back to the team

What do you need to succeed?

Must-Have

  • 5-8 yearsbuilding production software systems - you know what it takes to ship and operate reliable code at scale
  • 1-3 yearsworking hands-on with LLMs and generative AI in production (not just POCs)
  • Solid experience withagentic AI patterns- tool use, agent orchestration, ReAct, multi-step reasoning, memory and context management
  • StrongPythonskills and a commitment to writing clean, maintainable, production-grade code
  • Experience buildingRAG systemswith vector databases, embeddings, and retrieval strategies
  • Working knowledge ofMCPor similar agent-tool integration frameworks
  • Comfort withcloud platforms(AWS, Azure, or GCP), containers (Docker/Kubernetes), and CI/CD
  • Proven ability tolead technically- driving cross-team decisions, reviewing architecture, mentoring engineers
  • Clear communicator who can explain complex AI trade-offs to both technical and business audiences
  • Understanding ofsecurity, data governance, and responsible AIpractices

Nice-to-Have

  • Experience with agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI)
  • Hands-on with AI observability tooling (LangSmith, LangFuse, Weights & Biases)
  • Familiarity with multi-agent protocols (A2A) and the broader MCP ecosystem
  • Background in NLP, information retrieval, or knowledge graphs
  • Experience with prompt/context engineering at scale - versioning, evaluation, and reproducibility

What's in it for you?

  • Work on what matters- agentic AI, MCP, and multi-agent systems are the most exciting frontier in software right now, and you'll be building it for one of the largest enterprises in the country

  • Real impact, real visibility- your work will directly power AI capabilities in RBC

  • Access to serious resources- leading foundation models, rich enterprise datasets, and significant compute

  • Accelerate your growth- work alongside Staff and Principal Engineers, build deep expertise in agentic AI, and grow into technical leadership

  • A team that builds- collaborative, fast-moving, and deeply technical - we ship production AI, not slide decks

  • Competitive total rewards- compensation, performance bonuses, flexible benefits, and stock options where applicable

Job Skills

Agentic AI, AI Agent Orchestration, AI Systems, Cloud Platform, Collaboration, End-to-End Testing, Generative AI, Mentorship, Model Context Protocol, Production Software, Software Systems, Vector Databases

Additional Job Details

Address:

RBC CENTRE, 155 WELLINGTON ST W:TORONTO

City:

Toronto

Country:

Canada

Work hours/week:

37.5

Employment Type:

Full time

Platform:

TECHNOLOGY AND OPERATIONS

Job Type:

Regular

Pay Type:

Salaried

Posted Date:

2026-08-04

Application Deadline:

2026-08-24

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above

Our Employment Opportunities

At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.

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Expand your limits and create a new future together at RBC. Find out how we use our passion and drive to enhance the well-being of our clients and communities at jobs.rbc.com.

RBC is presently inviting candidates to apply for this existing vacancy. Applying to this posting allows you to express your interest in this current career opportunity at RBC. Qualified applicants may be contacted to review their resume in more detail.

Employment Type: FULL_TIME

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