Top Tech Jobs & Startup Jobs in Toronto

Supports commercial strategy and product pre-onboarding by defining operating models, mapping current and future processes, documenting requirements, analyzing capability gaps, and coordinating cross-functional stakeholders. The role creates process maps, user stories, governance materials, status reports, and implementation handoff packages while tracking risks, dependencies, decisions, and approvals across multiple initiatives.

Lead the development of AI-powered solutions, drive technical strategy and innovation, mentor engineers, and ensure quality and operational excellence.

Lead strategy, architecture, development, integration, and full lifecycle implementation of Conga CPQ, Turbo Price, Digital Commerce, and PROS Pricing solutions on Salesforce. Establish engineering standards, conduct code reviews, mentor developers, evaluate technologies, resolve complex issues, and ensure scalable, stable systems. Collaborate with architects, technical leads, cross-functional teams, and third parties while managing estimates, dependencies, risks, Agile delivery, and AI-enabled Agentforce capabilities.

Lead end-to-end change management for a multi-year commercial systems transformation: design strategy, templates, adoption metrics, and governance; oversee readiness across workstreams, engage stakeholders, advise launch decisions, and mentor a team to drive organizational adoption.

Lead and manage forward-deployed engineering teams to design, build, and deliver scalable, secure LLM/AI solutions for customers. Provide technical architecture guidance, drive MLOps/LLMOps best practices, oversee delivery of POCs and production systems, hire and develop engineers, and collaborate with Product, Sales, and Customer Success to shape product roadmaps and reusable AI frameworks.

Build and productionize AI agents for product analytics, integrating LLMs, tools, retrieval pipelines, and data systems. Develop data pipelines, evaluation frameworks, guardrails, telemetry, and monitoring. Diagnose agent quality issues, improve prompts and context, and contribute to proactive product intelligence systems. Collaborate with product and engineering teams to translate business needs into reliable agent designs while documenting work and following shared development standards.

Owns the build, hardening, patching, configuration, and lifecycle management of enterprise operating-system and platform infrastructure. Leads major incidents, root-cause analysis, change control, service improvement, and technical escalations. Identifies and scales AI and automation for incident triage, runbook execution, predictive monitoring, and reduced MTTR. Provides solution designs, implementation plans, standards, documentation, stakeholder influence, team mentoring, vendor coordination, and AI governance.

Leads a platform engineering team responsible for backend services, AI agent infrastructure, CI/CD, cloud infrastructure, observability, and operational reliability. Manages hiring, performance, career development, roadmap planning, architecture, production operations, incident response, and on-call participation. Guides scalable microservices and event-driven systems running on AWS and Kubernetes while partnering with product, applied AI, and infrastructure teams. Raises engineering standards across testing, deployment, monitoring, and maintainability.

Lead architecture and delivery of production AI agent systems: design agent infrastructure, observability, evaluation, progressive rollout, and reliability. Provide hands-on technical leadership, mentor senior engineers, drive RFCs/ADRs, and partner with product to sequence strategic platform work.

The Senior Applied Scientist will design, build, and deploy AI solutions for document understanding in the legal domain, leveraging NLP and deep learning methodologies. This role involves collaborating with product teams, advancing model capabilities, and ensuring robust training and evaluation methodologies.