Top AI Jobs in Toronto

Owns strategic relationships and long-term success for assigned automotive dealership accounts. Develops Customer Value Plans, leads outcome-focused Value Reviews, monitors adoption and account health, forecasts renewals, identifies expansion opportunities, and translates platform usage into measurable business value. The role coordinates with Sales, Product, Support, Enablement, and Implementation teams while representing customer needs internally. Travel to dealerships and customer events may be required.

MBA summer associates join Roland Berger for an 8–12-week internship, contributing to real client projects with consulting teams across global offices. Responsibilities involve strategic work, analytical and creative problem-solving, teamwork, entrepreneurial thinking, and applying AI-based solutions and data skills to complex business challenges. High-performing interns may receive full-time offers.

Design and build AI agents, automated workflows, and Atlassian configurations using Rovo Studio, Jira, Confluence, and Microsoft 365 Copilot. Translate business challenges into scalable processes, configure automations, create knowledge structures, monitor adoption, and promote responsible AI use. Develop documentation, playbooks, training, and enablement materials while collaborating with product, engineering, architecture, QA, delivery, and business teams.

Prototype, develop, deploy, and monitor AI applications for media production systems. Fine-tune and evaluate LLMs, integrate intelligent agents with services and APIs, connect AI capabilities to digital and legacy platforms, and support testing, security, and code quality. Collaborate with product, architecture, and data science teams while using AI-assisted coding tools to accelerate development.

Designs and builds AI agents and automated workflows using Atlassian platforms and Microsoft 365 Copilot. The role translates business needs into scalable process designs, configures Jira and Confluence, creates automation and knowledge structures, promotes responsible AI use, and supports adoption through documentation, training, demos, coaching, and stakeholder collaboration.

Leads Gartner research and thought leadership on applying AI and agentic capabilities across software engineering. Advises global software engineering executives on AI strategy, opportunity prioritization, roadmap planning, value measurement, ROI, and developer productivity. Produces research, market analysis, presentations, and client recommendations; supports sales and conferences; mentors analysts; and contributes to research strategy and process improvements.

Provides strategic research, thought leadership, and client advisory services on software engineering for AI-native applications and agentic systems. Develops market insights, analyzes trends, presents recommendations to executives, supports sales and conferences, reviews content, mentors analysts, and improves research processes. Requires deep practical knowledge of LLM applications, agentic AI frameworks, orchestration, production architecture, and emerging AI platforms. This remote individual contributor role requires 12+ years of relevant experience and travel up to 25%.

As a Generative AI Consultant, design and implement GenAI solutions, assist in architecture, develop applications, and ensure responsible AI practices while collaborating across teams.

Own the sales funnel for Benable’s Brand Portal, from prospect research and multichannel outreach through demos, follow-up, objection handling, and customer signup. Build repeatable AI-powered sales systems, automate recurring tasks, track pipeline performance, and improve messaging and channels based on results. Work directly with the founder and CEO to engage brand decision-makers and grow the company’s subscription product.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Leads a global AI engineering organization, owning multi-year strategy, product alignment, team transformation, vendor governance, and engineering operations. Oversees production AI lifecycle delivery, including retrieval, model integration, evaluation, deployment, monitoring, reliability, security, CI/CD, and AWS infrastructure. Establishes data-driven engineering metrics, AI adoption guardrails, cost controls, and responsible AI practices while partnering with product and technology leaders to deliver strategic initiatives.

Lead customer robot deployments from workflow discovery and site assessment through scoping, financial modeling, technical alignment, execution, acceptance testing, training, and handoff. Partner with engineering, research, safety, solution engineering, customers, and external teams to define requirements, success metrics, dependencies, and milestones. Translate field feedback into deployment practices while managing scope changes, implementation tradeoffs, customer value, utilization, and support needs.

Lead and manage a team building scalable full-stack legal research products, AI agents, retrieval-augmented generation systems, APIs, and cloud-native services. Provide hands-on technical leadership across architecture, coding, DevOps, security, reliability, and AI-assisted development. Hire, coach, and develop senior engineers while partnering with Product, Design, and stakeholders to deliver production-grade solutions on AWS.

Design, build, and deploy production-grade agentic multi-agent systems and stateful workflows. Implement memory and vector-based RAG pipelines, secure tool integrations, and cloud-deployed agents. Optimize LLM orchestration, enforce regulatory security and guardrails, collaborate with DevOps/RPA teams (UiPath), and maintain high code quality in Python/TypeScript.

Leads the architecture and delivery of large-scale AI/ML and GenAI solutions for global contact centers. Defines technical strategy, designs AWS-based APIs and infrastructure, establishes DevOps and CI/CD standards, manages model lifecycles, and ensures reliable production deployment. The role also mentors engineering teams, creates technical designs, evaluates emerging technologies, collaborates across teams, and supports troubleshooting and continuous improvement.

Leads CIBC’s AI Applications Development function, managing AI engineers, developers, and QA specialists. Provides technical and people leadership for designing, building, deploying, and supporting secure, scalable enterprise AI applications. Guides architecture, cloud-native delivery, CI/CD, observability, compliance, testing, and documentation. Partners with business and technology stakeholders to prioritize initiatives, translate needs into AI solutions, and advance a shared enterprise AI strategy while remaining hands-on with production development and technical problem-solving.

Develops and maintains software applications across the full lifecycle, translating business requirements into technical specifications, writing and testing code, troubleshooting systems, and managing releases. The role focuses on automation, AI, DevOps, cloud technologies, microservices, Salesforce, AWS, CDK, Python, observability, and machine learning. It also supports system design, security standards, integration, performance, documentation, and technology evaluations while collaborating with functional teams and providing technical consulting.

Build and operate scalable AI platform services, internal APIs, and production workflows integrating LLMs and AI tools. Own reliability through observability, alerting, incident response, performance tuning, cost optimization, capacity planning, CI/CD, and infrastructure improvements. The role requires Python backend development, distributed systems, asynchronous processing, cloud infrastructure, infrastructure as code, and cross-layer experience spanning services, data, infrastructure, and AI tooling.

Administer Microsoft Copilot and enterprise AI services, configure and maintain low-code/no-code agents, and support AI governance, security, privacy, and responsible use. Research emerging capabilities, evaluate tools, identify business use cases, and provide recommendations. Deliver demonstrations, workshops, documentation, coaching, and user support while monitoring adoption, service health, risks, and feedback. Collaborate with IT, business teams, vendors, and Microsoft stakeholders on pilots, adoption initiatives, and continuous improvement.

Create finished AI-generated video assets for broadcast and digital productions, from prompt development through polished delivery. Responsibilities include selecting and operating video models, building 3D-to-AI workflows, maintaining AI pipelines, ensuring visual continuity, performing technical QC, and collaborating with creative technologists and producers. The role requires professional production experience, advanced AI video-generation skills, and an expert understanding of visual storytelling, editing, and finishing.

Build production-ready AI applications, agents, APIs, data pipelines, integrations, and analytical models using Azure and Databricks. Develop LLM solutions with RAG, embeddings, vector search, tool calling, and agentic workflows. Establish testing, monitoring, governance, security, and responsible AI practices while partnering with consultants and subject-matter experts to translate business needs into reliable technical solutions.

Build production AI applications, agents, APIs, data pipelines, data models, and analytical solutions using LLMs, Azure, and Databricks. Develop RAG and agentic workflows, integrate structured and unstructured data, and implement testing, monitoring, security, governance, and responsible AI practices. Partner with consultants and subject-matter experts to translate business needs into practical solutions and move prototypes into reliable, scalable tools.

Build and productionize AI-powered applications, agents, APIs, data pipelines, integrations, and analytical models. The role combines LLM application development, RAG, data engineering, Azure and Databricks architecture, testing, monitoring, security, governance, and software delivery. Responsibilities also include partnering with consultants and subject-matter experts to translate business needs into practical AI solutions, prototype with users, and deploy reliable, scalable tools.

Leads organization-wide AI search and SEO enablement, developing GEO processes, digital PR, thought leadership, influencer, affiliate, and content strategies. Partners with Brand, Communications, Social, Influencer Marketing, and business teams to improve AI-powered discoverability, brand reputation, consumer trust, and category authority. Builds measurement frameworks tracking sentiment, share of voice, source authority, AI citations, content freshness, risk indicators, and brand mentions. Supports crisis communications and reputational risk management in a regulated environment.

Leads discovery and requirements gathering for enterprise contact center transformations. Designs, configures, prototypes, and validates Genesys Cloud AI solutions, including knowledge management, agent assistance, summarization, and virtual agents. Evaluates integrations, platform limitations, licensing, implementation costs, and operational requirements, then presents evidence-based recommendations to business and technical stakeholders.