Top AI Jobs in Toronto

Leads creative direction across digital, social, video, and brand campaigns while integrating generative AI into scalable design workflows. Develops concepts, prototypes, prompt libraries, and design systems; ensures brand consistency and quality of AI-generated content. Collaborates with data scientists, engineers, copywriters, animators, clients, and stakeholders. Presents visual strategies, mentors designers, explores emerging technologies, and promotes responsible AI use in creative production.

Build and deploy production-ready AI applications, including LLM integrations, RAG pipelines, agentic workflows, and automated triggers. Partner with strategy, sales, and operations teams to identify business opportunities, integrate AI systems with Snowflake, APIs, and third-party tools, optimize manual processes, and implement evaluation and monitoring frameworks. Communicate technical solutions to non-technical stakeholders while owning projects from discovery through deployment.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Provides technical leadership for agentic AI, ontology, knowledge graph, semantic, and enterprise data platform architecture. Designs reusable data models, ingestion pipelines, integrations, entity resolution, schema alignment, validation, and agent workflows across supply chain systems. Evaluates emerging technologies, establishes architecture and governance standards, mentors teams, and translates research into scalable product capabilities.

Build LanceDB’s revenue marketing function and enterprise demand engine from the ground up. Own account-based marketing strategy, segmentation, campaigns, content, account planning, martech implementation, pipeline reporting, and partner co-marketing. Identify high-intent accounts through open-source and product-usage signals, create tailored omnichannel programs, and collaborate closely with founders, Sales, RevOps, and Partners to source and influence enterprise pipeline.

Owns the technical strategy, roadmap, requirements, and delivery of scalable data solutions. Partners with business, product, design, and engineering teams to prioritize high-impact features, assess vendors, guide architecture, incorporate AI strategies, and resolve technical issues. The role requires strong BI and data warehouse expertise, technical leadership, stakeholder communication, agile product development, customer delivery support, and mentoring responsibilities.

Partners with AI labs to integrate models, run evaluations, deliver results, and manage customer relationships. Builds pragmatic full-stack solutions, automations, onboarding workflows, and delivery tooling for cutting-edge AI teams. Translates customer and research feedback into product and engineering initiatives, supports strategic account growth, and serves as a technical point of contact across onboarding, releases, and issue resolution.

Develop secure, scalable, production-ready AI applications, services, and APIs. Integrate machine-learning and generative AI models into enterprise systems, build data and retrieval workflows, develop prompts, and implement testing, monitoring, logging, and evaluation. Collaborate with product, data, platform, and engineering teams while applying security, privacy, responsible-AI, and governance practices. Troubleshoot issues, document technical designs, participate in code reviews and Agile ceremonies, and support continuous improvement.

Own end-to-end AI product development for enterprise clients, from requirements and rapid LLM prototyping through production deployment. Responsibilities include data modeling, ETL pipelines, integrations, evaluation, observability, security, reliability, cost management, and client communication. The role requires shipping real AI products, iterating based on usage, and independently managing ambiguous technical and product decisions.

Build and scale AI-agent infrastructure, including orchestration, tool calling, context management, guardrails, evaluations, and LLM-as-judge pipelines. Develop Python and Node.js microservices, Laravel APIs, AWS infrastructure, and optimized MySQL systems. Diagnose agent failures, improve prompts and tools, maintain reliability and observability, contribute to architecture, review code, collaborate cross-functionally, and mentor engineers.

Partner with executives to understand business operations, identify and prioritize high-impact AI opportunities, define ROI, and create actionable roadmaps. Collaborate with engineers to scope solutions, inform architecture, maintain alignment with business outcomes, present recommendations to C-suite stakeholders, and manage handoffs to implementation.

Lead design, development, and deployment of large-scale Python-based Generative AI platforms and services. Drive architecture, stakeholder engagement, vendor partnerships, CI/CD, and governance. Manage multiple complex projects, mentor mid-level engineers, and ensure risk, monitoring, and integration across Citi business units.

Supports responsible AI governance by developing policies, maintaining control frameworks, conducting AI risk assessments, monitoring regulatory compliance, preparing reports, and advising legal, compliance, security, AI, and business stakeholders. The role also tracks governance metrics, supports audits, researches emerging regulations, and recommends improvements to AI governance programs.

Design, develop, deploy, and optimize generative AI applications using LLMs and AI frameworks. Build scalable APIs, integrate foundation models and third-party platforms, automate business workflows, and connect AI solutions to internal systems and data sources. Monitor model quality, improve prompts and configurations, support responsible AI governance and security, evaluate emerging technologies, and collaborate with product, engineering, and business teams.

Designs, implements, and optimizes AI-powered workflow automation solutions. Responsibilities include analyzing business processes, documenting current and future-state workflows, identifying automation opportunities, gathering requirements, integrating automation with business systems, supporting testing and deployment, monitoring performance metrics, troubleshooting issues, training users, and promoting automation best practices and continuous improvement.

Leads the architecture, development, optimization, deployment, and lifecycle management of enterprise machine learning systems. Establishes AI engineering standards, MLOps practices, data quality processes, model governance, security, and validation frameworks. Provides technical leadership, mentors engineers, evaluates emerging technologies, and supports AI transformation initiatives while partnering with data scientists, engineering, product, and business teams.

Develop, optimize, test, and maintain prompts for conversational AI, automation, and business applications. Evaluate AI outputs, conduct prompt experiments, improve response quality, maintain prompt libraries, document standards, and support governance. Collaborate with AI engineers, trainers, product teams, and business stakeholders to translate requirements into prompting strategies and support Generative AI implementations. Research emerging techniques, evaluate tools, and recommend improvements to AI quality and effectiveness.

Leads AI data annotation operations, managing workflows, resources, productivity, quality assurance, and project timelines. Coaches and develops annotation teams, supports recruitment, and collaborates with AI engineers, trainers, and product teams. Establishes annotation guidelines, conducts audits, resolves quality issues, and drives process improvements, automation, tool adoption, and operational excellence across AI data initiatives.

Lead the organization’s AI workflow automation strategy, identify and prioritize automation opportunities, design scalable AI-powered workflows, and manage initiatives from concept through implementation. Partner with business and technical stakeholders, track budgets and timelines, establish governance, measure ROI and operational outcomes, develop performance dashboards, evaluate emerging technologies, and drive continuous improvement, adoption, and organizational transformation.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering and evaluation strategies, and ensures secure, scalable production deployments. Partners with executives and business leaders to define AI roadmaps, mentors engineering teams, evaluates emerging technologies, and drives proof-of-concept initiatives and responsible AI practices.

Labels, categorizes, validates, and quality-checks structured and unstructured data used to train AI and machine learning models. Maintains dataset accuracy, consistency, documentation, and version control while following detailed annotation guidelines. Collaborates with AI engineers, data scientists, and quality teams, identifies errors, provides process feedback, and supports continuous improvement of annotation workflows.

Reviews AI-generated content for accuracy, relevance, quality, safety, compliance, and adherence to organizational standards. Identifies factual inconsistencies, bias, harmful outputs, and recurring issues; documents findings and escalates concerns. Provides structured feedback to AI trainers and prompt engineers, tracks quality metrics, prepares reports, supports root-cause analysis, and helps improve prompts, training processes, and review guidelines.

Develop and manage the strategy, roadmap, lifecycle, and delivery of AI-powered products. Translate business needs into product requirements, collaborate with engineering and AI teams, define success metrics, monitor adoption and ROI, and drive continuous improvements. The role also engages customers and stakeholders, evaluates emerging technologies, supports governance and compliance, and promotes responsible AI practices.

Designs, tests, evaluates, and optimizes prompts for large language models and AI applications. Develops reusable prompt templates, conducts experimentation and A/B evaluations, monitors AI performance, and establishes quality standards. Collaborates with AI engineers, product managers, trainers, and business stakeholders to translate requirements into scalable prompting strategies. Researches generative AI advancements, supports model evaluation and tuning, documents best practices, and recommends improvements to response quality, consistency, accuracy, and business relevance.

Designs, tests, evaluates, and refines prompts for generative AI applications across customer support, content generation, workflow automation, knowledge management, and productivity use cases. The role evaluates AI outputs for accuracy, relevance, tone, and consistency; documents testing results; maintains prompt libraries and guidelines; collaborates with AI engineers, product teams, data scientists, and business stakeholders; and researches emerging AI tools and prompting techniques.