AI Engineer

• Design and plan CBC/Radio-Canada’s future technology infrastructure
• Design and build high-performance training and inference systems for LLMs and multimodal AI models
• Optimize training data pipelines, distributed training, mixed precision, inference engines, KV caching, batching, quantization and long-context processing
• Design, right-size and evolve the internal GPU cluster
• Architect AI integration within media production environments, including PAM, MAM, television and radio studios
• Evaluate and standardize integration protocols such as MCP and A2A
• Interconnect AI models with Avid, Dalet and Adobe production platforms
• Develop secure video understanding systems for file-based and IP live workflows
• Provide expert guidance to improve scalability, latency and reliability
• Mentor engineers and data scientists in large-scale ML system design and performance engineering
• Develop MLOps/LLMOps pipelines with observability, performance profiling and automated testing
• Explain technological innovations to decision-makers and production teams and influence strategic investments

AI Architect – T & I

• Lead the design and development of production-grade autonomous and multi-agent AI systems
• Provide technical leadership and enterprise AI architecture
• Develop AI agents supporting software development workflows, including requirements, coding, testing, code reviews, and CI/CD

AI Architect – AI Engineering Lead

• Own the end-to-end architecture of the Snowflake Cortex RAG pipeline, aligned with Enterprise TPRM Standards
• Act as the primary technical liaison to client business owners, AI leads, and InfoSec SMEs
• Lead UAT sessions with InfoSec SMEs and convert feedback into prioritized technical backlog items
• Prepare the Model Development Documentation packet and drive AIEC-2 clearance before production deployment
• Direct daily agile standups and sprint planning for the three-person offshore pod
• Ensure milestone delivery within the SOW budget

Lead AI Architect, Delivery Manager

• Lead the design, implementation, and operation of security controls for AI, generative AI, machine learning, AI agents, copilots, automation platforms, orchestration frameworks, model providers, and supporting cloud services
• Report to the Senior Director, Cyber Security Engineering & Operations
• Work as a fully dedicated, embedded security partner to the AI Centre of Enablement and Enterprise Technology, Data & AI organization
• Develop reusable security patterns, guardrails, control implementations, automation, and detection capabilities for enterprise AI adoption
• Partner with AI Centre of Enablement, AI Engineering, Data Engineering, Technology, Cyber, Architecture, Data, Engineering, and business teams
• Implement data protection, identity, access, model access, monitoring, and detection controls
• Review solution designs and provide guidance on risks, control gaps, and regulatory requirements
• Perform and support adversarial testing and prioritize remediation of security weaknesses
• Assess AI supply-chain risks involving models, plug-ins, agents, and third-party AI services
• Support AI governance, compliance, audit, and risk management activities
• Create technical documentation, standards, procedures, and implementation guidance
• Mentor engineers, architects, delivery teams, and technical and non-technical stakeholders
• Engage vendors and external partners to validate security capabilities
• Serve as a senior individual contributor with no direct reports

Staff Engineer, AI Security

• Own technical direction for AuthN, AuthZ, and OBO across the Agentic Data Platform and Cloud platform
• Decide which emerging industry patterns Redpanda adopts, adapts, or avoids
• Extend the OAuth 2.1 authorization server and token vault
• Build delegated tool-access flows and a cryptographic delegation model for auditable on-behalf-of authority chains
• Partner with identity-federation systems including Okta, Entra, Ping, and similar providers
• Collaborate with Agentic Data Plane, Cloud platform engineering, Security, and Product
• Represent Redpanda's technical position in customer security conversations, documentation, and standards efforts
• Ship production-hardened systems to customers
• Mentor engineers across Agentic Data Platform and Cloud platform without formal management authority
• Escalate systemic challenges and impediments to engineering leadership
• Discuss strategic topics with peers and Directors
• Track progress, assess risks, and communicate contingency and mitigation plans

Staff Software Engineer, Identity & Access – AI Platform

• Take a leading technical role in the FME AI Service, AI Assist Plugin, and future AI product offerings
• Design and build server-side services and APIs using FastAPI
• Design and build modular agentic workflows to augment FME platform capabilities
• Stay current with AI and LLM tooling and incorporate relevant ideas into products
• Participate in the full development cycle, including specifications, coding, unit and integration testing, code reviews, and maintaining existing code
• Collaborate, support, and share expertise with team members
• Work with product owners and design teams to deliver a high-quality, consistent user experience
• Maintain and improve the AI service build and delivery process to reduce query latency and improve assistant response quality
• Investigate, evaluate, and resolve anomalies and bugs in live applications
• Participate in the team's light on-call rotation
• Participate in agile team meetings, including stand-ups and planning
• Lead technical decision-making processes
• Work with Product Managers, UX Designers, QA, and Customer Experience team members reviewing live AI application traces

Staff Software Developer – AI Products

• Design, build, test, deploy, and support production-ready AI applications and defined technical workstreams
• Translate customer and business requirements into component designs, implementation plans, estimates, acceptance criteria, and technical trade-offs
• Develop AI solutions using LLMs, RAG, semantic search, document/data processing, and agentic workflows
• Build secure backend services, APIs, integrations, and cloud-native components connecting AI applications with enterprise systems and data sources
• Design and optimize ingestion, chunking, embeddings, retrieval, prompting, grounding, tool use, memory, and workflow orchestration
• Evaluate models, platforms, and solution approaches based on quality, latency, cost, scalability, security, privacy, and operational requirements
• Productionize AI solutions through automated testing, resiliency, observability, performance optimization, evaluation, monitoring, and responsible AI controls
• Contribute to CI/CD, infrastructure-as-code, containerized/serverless deployments, and MLOps/LLMOps practices
• Participate in architecture, design, and code reviews; create reusable components; improve engineering standards; and produce technical documentation and runbooks
• Collaborate with customers, architects, engineers, data teams, and business stakeholders through discovery, technical design, demonstrations, estimates, and knowledge transfer
• Help organizations turn ambition into real-world outcomes through secure cloud, AI, software, and digital workplace solutions

Software Engineer, AI

• Own complex, strategic accounts and build trusted relationships with executives, clinicians, and operational leaders
• Align stakeholders around transformation plans and measurable outcomes
• Identify and advance opportunities for adoption, expansion, and renewal
• Lead live product demonstrations, discovery, and solution scoping
• Partner with Clinical Engineers on technical design and implementation
• Solve ambiguous deployment challenges by identifying root causes, developing options, and recommending next steps
• Deliver complex initiatives end to end, coordinating customer and internal-team dependencies
• Track progress, surface risks early, and adjust plans to protect delivery and customer value
• Use Heidi products routinely and apply knowledge of the roadmap, architecture, and clinical logic
• Bring customer workflows, pain points, and buying processes into Product and Clinical Engineering discussions
• Develop use cases that increase meaningful usage
• Turn successful approaches into repeatable deployment, adoption, and account-growth practices
• Mentor less experienced team members and connect teams
• Maintain forecasts and account plans using adoption, utilization, and credit-consumption data

Applied AI Lead – Account Management

• Build the core AI agent runtime powering conversations across voice, chat, and email in multiple languages
• Integrate real-time access to gaiia’s APIs for account lookups, billing, plan changes, and other operations
• Design and implement the agent configuration experience for behavior, tone, escalation rules, and business logic
• Build quality and observability tooling, including dashboards, conversation analytics, and automated evaluation systems
• Develop continuous improvement loops that analyze customer interactions, identify gaps, suggest improvements, and validate changes
• Integrate voice, live chat, email, and SMS infrastructure
• Design guardrails and safety layers to enforce approved boundaries and appropriate escalation
• Contribute to automated conversation simulations, regression testing, and A/B testing infrastructure
• Work in a serverless ecosystem of microservices deployed on cloud infrastructure using TypeScript across frontend, backend, and infrastructure-as-code

Senior Full-Stack Software Developer – AI Customer Support

• Embed with one or two teams per cycle and own each AI solution end to end, from intake brief to a shipped, monitored production feature
• Decide whether to build, wrap, or vet each initiative and set the validation bar
• Build the agentic layer, including tool and function calling, RAG, MCP integrations, multi-step orchestration, and human-in-the-loop checkpoints
• Architect on the Prenuvo AI platform, including shared model serving, AWS Bedrock foundation-model contracts, evaluation harnesses, monitoring, tool registry, and prompt guardrails
• Define acceptance criteria, offline and online evaluations, and escalation triggers before building
• Run initiatives through AI intake and risk categorization processes
• Design solutions to governance standards while Regulatory, Privacy, and Legal provide formal sign-off
• Leave reusable assets such as validated templates, registered tools, or reference architectures
• Raise AI fluency through design reviews and office hours
• Enable more teams to ship AI features independently using templates and champions

AI Engineer jobs in Canada