Top Tech Jobs & Startup Jobs in Toronto

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.

Develop and execute staffing sales strategies, build and manage client relationships, generate and qualify leads, develop business pipelines, schedule prospect appointments, close deals, and coordinate qualified leads with internal teams. The role also provides client staffing recommendations, oversees regional sales initiatives, and mentors direct reports through performance management.

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.

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.

Designs and maintains Retrieval-Augmented Generation systems, including document ingestion, indexing, semantic search, vector databases, embedding pipelines, and LLM integrations. Develops APIs and scalable knowledge systems, optimizes retrieval accuracy and performance, monitors production environments, and implements governance and security controls. The role also evaluates emerging retrieval technologies, troubleshoots indexing issues, documents procedures, and collaborates with AI engineers and product teams.

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.

Leads the design, development, and optimization of enterprise Retrieval-Augmented Generation platforms. Responsibilities include building retrieval architectures, ingestion and indexing frameworks, semantic and vector search systems, evaluation methods, scalable APIs, and LLM integrations. The role also covers security, governance, compliance, monitoring, troubleshooting, documentation, technical leadership, mentoring, and evaluating emerging AI retrieval technologies.

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.

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.

Own AI product backlogs, priorities, user stories, acceptance criteria, and delivery outcomes. Translate business needs into product requirements, collaborate with technical teams on solution design, facilitate stakeholder communication, support Agile delivery, and monitor product performance and adoption. The role also identifies AI-driven improvements, supports product releases, drives continuous improvement, and promotes responsible AI practices.