Software engineer jobs in Toronto, ON and nearby

Software engineer jobs within 25 km of Toronto, ON, Canada. Nearby towns and cities are included. Distances are straight-line, not driving distances.

Counted Sep 24, 2026

Hiring at a glance

View jobs

Open jobs: 999
Employers: 379

Advertised starting pay

Average starting payCAD / year: $129,758Across 102 listings
Average starting payUSD / year: $156,528Across 39 listings
Average starting payCAD / hour: $66.50Across 16 listings

Employer-advertised starting pay, not employee salaries. Currencies and pay periods stay separate.

Where work happens

Remote: 0%0 listings
Hybrid: 52%523 listings
On-site: 46%458 listings

Not specified: 2% (18 listings)

Employers hiring include Amazon (46), Scotiabank (28), Okta (26).

Sources & calculation notes

These figures describe the listings on this role page at the date shown, not the whole job market. Listings can change.

Employer-listed minimums from at least 10 listings per currency and pay period. These are advertised starting amounts, not employee salaries. Jobs without a minimum and unusual amounts are excluded.

Hybrid and on-site classifications may be inferred from descriptions. Check the original ad. How we count

About these jobs

Before you apply

Look at the required languages, systems and experience in each ad. Job Bank describes typical education and programming experience for this occupation. A junior title does not mean every employer accepts applicants without experience. Job Bank / Guichet-Emplois.

Guidance reviewed on September 16, 2026.

Based on the dated count above. Listings can change. How we count

Software engineer jobs in Toronto, ON and nearby

Within 25 km

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AstraZeneca

Generative AI Cloud Operations Engineer - Evinova

Mississauga, ON, Canada
Canada - Mississauga, Canada

Job type: Full-time
Apply by: Oct 9, 2026
Education: High school diploma
Experience: Minimum of 2 years of hands-on experience deploying, operating, and maintaining Generative AI agents, workflows, or applications in production environments.
Posting language: English
Posted: Sep 20, 2026

Apply About employer

Applications open in a new tab.

Original job description

WHY JOIN US?

Evinova is a health-tech business focused on accelerating better health outcomes by advancing digital transformation across the life sciences sector. By combining science-based expertise, evidence-led rigor, and deep human insight, we design digital solutions that enable healthcare to work better for everyone.

Operating at the intersection of healthcare, technology, data, and analytics, we are helping unlock the full potential of digital health, transforming how clinical research is conducted, how care is delivered, and how patients experience healthcare. Our solutions are built to scale, driving efficiency, improving decision-making, and ultimately delivering better outcomes for patients worldwide.

At Evinova, we are driven by a shared purpose to transform health through data and digital innovation. Our teams collaborate across disciplines to solve complex challenges, continuously learning and evolving in a fast-paced, high-impact environment.

We also recognize the importance of flexibility and balance. Our ways of working support both individual needs and team collaboration. To foster connection and collaboration, employees are expected to work from the office three days per week, creating opportunities for in-person teamwork, innovation, and meaningful connection.

Introduction to Role:

The Machine Learning and Artificial Intelligence Operations team (ML/AI Ops) is a newly formed platform team that will spearhead the design, creation, and operational excellence of our LLM-based agent deployments, multi-agent orchestration, and conversational AI systems pipelines to catalyze and accelerate science led innovations.

This team is responsible and accountable for the design, implementation, deployment, health and performance of all LLM-based applications. We manage ML/AI and broader cloud resources, automating operations through infrastructure-as-code and CI/CD pipelines, and ensure best-in-class operations – striving to push even beyond mere compliance with industry standards such as Good Clinical Practices (GCP) and Good Machine Learning Practice (GMLP).

As a Generative AI Cloud Operations Engineer for clinical trial design, planning, and operational optimization on our team, you will lead the development and management of AI operations systems for our trial management and optimization SaaS product. You will collaborate closely with our AI Engineers to transition projects from embryonic research into production-grade AI capabilities, utilizing advanced tools and frameworks to optimize model deployment, governance, and infrastructure performance.

This position requires a deep understanding of cloud-native agentic Generative AI deployment methodologies and technologies, AWS infrastructure, and the unique demands of regulated industries, making it a cornerstone of our success in delivering impactful solutions to the pharmaceutical industry.

Accountabilities:

Operational Excellence

Drive the creation of proactive capability and process enhancements that ensures enduring value creation and analytic compounding interest.
Design and implement resilient cloud Genereative AI agent operational capabilities to maximize our system A-bilities (Learnability, Flexibility, Extendibility, Interoperability, Scalability).
Drive precision and systemic cost efficiency, optimized system performance, and risk mitigation with a data-driven strategy, comprehensive analytics, and predictive capabilities at the tree-and-forest level of our Generative AI-based systems, workloads and processes.

ML/AI Cloud Operations and Engineering

Develop and manage GenAI Ops systems for clinical trial design, planning and operational optimization.
Integrate LLM proxies/routers including LiteLLM Proxy/Router or other solutions
Ensure proper RAG pipeline optimization and scaling
Integration of token usage, latency, response quality, and hallucination detection tools at a platform level.
Partner closely with AI Engineers and data scientists to shepherd projects from embryonic research stages into production-grade agentic Generative AI capabilities.
Leverage and teach modern tools, libraries, frameworks and best practices to design, validate, deploy and monitor Generative AI agents in production (including LangChain, LangGraph, Google ADK, Langfuse, DSPy, Arize Phoenix, Pinecone, Weaviate, Splunk, Grafana, Prometheus, Xray, and more)
Enhance system scalability, reliability, and performance through effective infrastructure and process management.
Ensure that any prediction we make is backed by deep exploratory data analysis and evidence, interpretable, explainable, safe, and actionable.
Leverage Vertex AI, Azure Foundry, OpenAI, Anthropic, and other foundation model platforms to provide reliable and stable access to LLMs

Personal Attributes:

Customer-obsessed and passionate about building products that solve real-world problems.
Highly organized and detail-oriented, with the ability to manage multiple initiatives and deadlines.
Collaborative and inclusive, fostering a positive team culture where creativity and innovation thrive.
Know when to ask for help and when to help others proactively.

Essential Skills/Experience:

High school diploma or GED required.
Minimum of 2 years of hands-on experience deploying, operating, and maintaining Generative AI agents, workflows, or applications in production environments.
Strong understanding of the challenges associated with production GenAI systems, including reliability, scalability, latency, cost optimization, observability, evaluation, and model performance.
Hands-on experience deploying agentic AI solutions using frameworks such as LangChain, LangGraph, LlamaIndex, Google ADK, Strands Agents, or similar.
Strong experience with LLM evaluation and observability, using platforms such as Arize Phoenix, Langfuse, Braintrust, Freeplay, or comparable tools.
Strong software engineering skills in Python and/or TypeScript, with experience building production-quality systems.
Deep expertise with AWS cloud services, including deploying and operating cloud-native AI/ML workloads.
Strong experience with infrastructure as code, including AWS CDK using Python and/or TypeScript.
Experience with containerization and orchestration technologies, including Docker and Kubernetes.
Strong understanding of the data science and machine learning lifecycle, with demonstrated experience moving models and AI capabilities from experimentation through production deployment and ongoing operations.
Experience operationalizing RAG pipelines, LLM applications, or multi-agent systems in production is strongly preferred.
Demonstrated ability to stay current with rapidly evolving Generative AI models, frameworks, tooling, evaluation techniques, and engineering practices.
Proven ability to partner effectively with AI/ML engineers, data scientists, software engineers, product teams, and other cross-functional stakeholders.
Strong written and verbal communication skills, with the ability to clearly document technical solutions, operational processes, and system performance.

SO, WHAT’S NEXT?

To be considered for this exciting opportunity, please complete the full application on our website at your earliest convenience – it is the only way that our Recruiter and Hiring Manager can know that you feel well qualified for this opportunity. If you know someone who would be a great fit, please share this posting with them.

Where can I find out more?

Explore what we’re building: www.evinova.com
Stay connected and see our impact in action: https://www.linkedin.com/company/evinova/

Apply today to bring smarter, faster clinical trials to life!

Evinova is an equal opportunity employer that is committed to diversity and inclusion and providing a workplace that is free from discrimination. Evinova is committed to accommodating persons with disabilities. Such accommodation is available on request in respect of all aspects of the recruitment, assessment and selection process and may be requested by emailing AZCHumanResources@astrazeneca.com.

⠀

Annual base salary for this position ranges from 134,855.20 to 176,997.45.

AstraZeneca is committed to providing fair and equitable compensation opportunities to all colleagues. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The range provided in this posting represents an offer pay range used in a majority of situations. The base pay offered will vary depending on multiple individualized factors, including the candidate's skills and experience, job-related knowledge, and other specific business and organizational needs. In some cases, offers outside the range may also be considered to address unique circumstances.

In addition, our permanent positions offer an annual Variable Pay Bonus/Short Term Incentive opportunity as well as eligibility to participate in our equity-based long-term incentive program (if applicable to role). Benefits offered for permanent roles include a competitive Flex Benefits & Retirement Savings Program, 4 weeks’ paid vacation, and annual Personal Days. Fixed Term Contract/Temporary positions (excluding students) are offered a Contract Benefits Program.

We are using AI as part of the recruitment process.

This advertisement relates to a current vacancy.

Apply About employer

More about this job

Responsibilities

The role involves designing, deploying, and managing LLM-based agent systems and cloud infrastructure to support clinical trial optimization. You will collaborate with AI engineers to transition research projects into production-grade, scalable, and reliable AI capabilities.

Requirements

Candidates must have at least 2 years of hands-on experience in production Generative AI operations and strong software engineering skills in Python or TypeScript. Proficiency with AWS cloud services, infrastructure as code, and LLM evaluation frameworks is essential for this position.

Skills

Generative AI
Cloud Operations
AWS
Python
TypeScript
LangChain
LangGraph
Kubernetes
Docker
RAG pipelines
LLM evaluation
Infrastructure as Code
AWS CDK
Observability
Machine Learning Lifecycle

Benefits

Variable Pay Bonus
Short Term Incentive
Equity-based long-term incentive program
4 weeks paid vacation
Personal Days

Visa sponsorship

Not detected in the job text

Keywords

Generative AI
Cloud Operations
AWS
Python
TypeScript
LangChain
LangGraph
Kubernetes
Docker
RAG
LLM
Arize Phoenix
Langfuse
Infrastructure as Code
AWS CDK
Vertex AI
Azure Foundry
Clinical Trials
SaaS
Observability
Machine Learning
Data Science
CI/CD
Multi-agent orchestration
Conversational AI
GCP
GMLP
Splunk
Grafana
Prometheus

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