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Generative Ai Testing Jobs in Ontario (NOW HIRING)

BDO Canada

AI Engineer - Generative & Agentic AI

London, ON · On-site

CA$84K - CA$128K/yr

... Generative and Agentic AI solutions . This new role is ideal for someone who thrives at the ... Establish best practices for testing, performance tuning, and evaluation of agentic/LLM-based ...

BDO Canada

AI Engineer - Generative & Agentic AI

Ottawa, ON · On-site

CA$84K - CA$128K/yr

... Generative and Agentic AI solutions . This new role is ideal for someone who thrives at the ... Establish best practices for testing, performance tuning, and evaluation of agentic/LLM-based ...

Guidepoint

AI Engineer

Toronto, ON · On-site

You will develop and scale Generative AI-powered systems, including large language model (LLM ... testing, CI/CD (e.g., ArgoCD), observability, monitoring, alerting, maintaining high uptime, and ...

Guidepoint

Senior Data/AI Engineer

Toronto, ON

You will develop and scale Generative AI-powered systems, including large language model (LLM ... testing, CI/CD (e.g., ArgoCD), observability, monitoring, alerting, maintaining high uptime, and ...

KPMG

Manager, ML/AI Engineer, Data & AI

Toronto, ON · On-site

... automated testing, and monitoring. * Implement model monitoring, performance tuning, drift ... Experience with generative AI, large language models (LLMs), prompt engineering, or ...

Source Code

RQ11252 - Sr. AI Engineer

Toronto, ON

CA$90.18 - CA$108.22/hr

Hands-on experience implementing Generative AI and Large Language Model (LLM) solutions in ... Participates in testing, troubleshooting, deployment and ongoing optimization of AI-powered contact ...

Accenture

AI Full Stack Developer

Ottawa, ON

CA$114K - CA$171K/yr

Design, develop, and implement Generative AI agents to enhance user experience and automate ... Establish and uphold coding standards, testing protocols, and quality assurance processes across ...

Accenture

AI Full Stack Developer

Toronto, ON

CA$114K - CA$171K/yr

Design, develop, and implement Generative AI agents to enhance user experience and automate ... Establish and uphold coding standards, testing protocols, and quality assurance processes across ...

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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.

Senior Generative AI Engineer

Inizio Partners

Toronto, ON

Full-time

Re-posted 8 days ago

Job description

Senior Generative AI EngineerBackground

We are looking for a Senior Generative AI Engineer to design, build, and ship production-grade Generative AI and Agentic AI applications that delivery business value across the organization. This role is focused on building AI applications and services at scale. You will be responsible for building robust, secure, and highly scalable systems that integrate with leading cloud-based AI services.

You will work alongside product managers, designers, and other engineers as an individual contributor. You are expected to own features end-to-end, and to deliver high-quality, reusable code that scales beyond a single use case.

Key ResponsibilitiesSoftware Engineering and Execution
  • Design, build, and ship production-grade Generative and Agentic AI features and applications
  • Own features end-to-end from technical design through implementation, testing, deployment and operation
  • Build reusable, well-abstracted components and shared utilities (e.g., RAG building blocks, agent scaffolding, evaluation harnesses, prompt utilities) to enable faster delivery of future Generative and Agentic AI products
  • Build multi-agent systems using frameworks such as LangChain, LangGraph, Claude Agent SDK and Google ADK
  • Integrate with leading LLM and foundation model APIs, including Azure OpenAI, Google Vertex AI, and AWS Bedrock
  • Build Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking strategies, embeddings, vector search, and re-ranking
  • Build clean, well-tested RESTful and/or gRPC APIs with a focus on reliability, security, and performance
  • Implement observability, tracing, evaluation, guardrails for Generative and Agentic AI applications
  • Deploy and operate services on major cloud providers (e.g., GCP, AWS, and Azure) leveraging managed services
  • Participate actively in code reviews and design discussions, sharing knowledge with peers
Required Qualifications
  • 5-7 years of professional software engineering experience with at least 3 years of experience building AI/ML software products
  • Bachelor's degree in Computer Science or a related field (Master's degree preferred)
  • Strong proficiency in Python, with deep software engineering fundamentals (abstraction, modularity, system design, testing, performance)
  • Hands-on experience building and shipping Generative and Agentic AI applications, including LLM integration, prompt engineering, and/or agentic workflows
  • Practical experience integrating cloud-hosted LLM APIs such as Azure OpenAI, Vertex AI, and/or AWS Bedrock
  • Experience with agent frameworks (e.g., LangChain, LangGraph, Google ADK, Claude Agent SDK) and vector databases (e.g., Pinecone, Weaviate, pgvector, Open Search, AlloyDB)
  • Hands-on experience with Google Cloud Platform (GCP), Amazon Web Services (AWS), or Azure
  • Solid understanding of API design, distributed systems, and cloud-native architecture
  • Track record of taking systems from design through production deployment and operation
Preferred Qualifications
  • Experience with containerization and orchestration (Docker, Kubernetes)
  • Knowledge of Generative AI Risk Management frameworks (NIST RFM)
  • Experience supporting developer platforms or internal tooling