AI Engineer

• AI Implementation - Design, build, and maintain all of our AI systems across the company. Everything from internal marketing, sales, finance, and operations systems to local AI systems in our engineering workflows, and the Chamber itself.
• AI Adoption - Educate, Train, Support, and Uplift everyone in the company with their AI adoption. Running office hours, making tutorials, providing resources, sourcing solutions, and more.
• AI Infrastructure - Architect and operate a scalable set of infrastructure that allows non-technical, and technical teams to use AI in a secure and productive manner.
• AI Systems & Agents - In conjunction with departments, design, build and maintain all AI systems and agents. This includes administering and managing our off the shelf AI systems like Claude, Viktor, Cursor, etc. It also includes our custom built Agents, Skills, Plugins, Tools, and more.
• AI Security - You are responsible for creating, maintaining, and enforcing all data, privacy, and security policies across our AI systems and Agents. Organization, Department, and Role based permissions and settings across all our products.
• Other Projects - As this is a technical role, you will also be asked to help build other internal systems like automations, micro apps, and more.

AI Solutions Lead

• Design and plan CBC/Radio-Canada’s future technology infrastructure
• Optimize Models, Inference and GPU Infrastructure
• Design and build high-performance training and inference systems for LLMs and multimodal AI models
• Collaborate with the Technology & Infrastructure (T&I) team to design, right-size and evolve our internal GPU cluster
• Plan and develop the integration of AI solutions within CBC/Radio-Canada’s media production environments
• Mentor and elevate the organization’s engineers and data scientists in large-scale ML system design and performance engineering

AI Architect – T & I

• Design, develop, and maintain AI agents and pipelines that integrate language models (LLMs) into member-facing features as well as internal tools
• Monitor performance, cost, reliability, and result quality of agents and pipelines in production, and implement the necessary tools to ensure their observability
• Integrate language model (LLM) APIs and third-party AI services into our Ruby on Rails environment and existing systems
• Evaluate and select the most appropriate AI tools, models, and services, considering their capabilities, cost, and reliability
• Collaborate with cross-functional teams to identify opportunities where AI can enhance member experience or optimize internal processes
• Diagnose issues and continuously improve deployed solutions in a fast-evolving field where models, tools, and best practices change rapidly

AI Developer

Machine Learning Engineer

• Develop and oversee implementation of strategic AI roadmaps aligned with organizational objectives and deliver innovative solutions for complex business challenges
• Direct cross functional collaboration, building strong relationships to drive impactful outcomes and influence enterprise strategies
• Lead multiple AI focused initiatives, ensuring operational efficiency, resource optimization, and alignment with corporate strategic plans
• Provide strategic direction and leadership to teams, ensuring successful execution of high impact AI projects and contributing to long term objectives
• Manage AI Engineering initiatives of significant complexity, applying advanced technical expertise to solve abstract problems
• Make independent decisions on program, technical or operational strategy for the department
• Drive innovation across areas of responsibility identifying opportunities for operational changes and practices
• Lead cross functional collaboration efforts, fostering strong internal relationships across the organization and external relationships to drive business outcomes

Staff Machine Learning Software Engineer

• Evolve the Interactive Learning Plugin's recommendation system
• Develop increasingly personalized approaches to candidate selection, ranking, sequencing, and next-best-action recommendations.
• Build and operate applied models: develop, validate, version, monitor, and iterate on models used by the recommendation system.
• Define what recommendation quality means: develop offline, online, and longitudinal measures of recommendation performance.
• Ship incremental improvements: use existing data and infrastructure while identifying future instrumentation needs.
• Partner across disciplines: collaborate with software engineers & data analysts to productionize models.

Senior Machine Learning Engineer, Developer Advocacy

• As a Senior Engineer within the Internal AI Accelerator Squad, you will be at the forefront of Xero’s transformation into an AI-native company.
• You will act as a forward-deployed engineer, embedding yourself within various internal business teams to co-design and operationalise agentic AI workflows that remove manual toil and fundamentally reshape how our people work end-to-end.
• You will lead the delivery of high-stakes AI agents from discovery to production, while acting as a technical partner and evangelist.
• By defining the patterns, guardrails, and tooling for intelligent automation, you will create the blueprint that enables the rest of Xero to harness the power of AI safely and effectively.
• You will join a dedicated, high-velocity squad focused on boosting the adoption of agentic AI and intelligent automation across the global business.

Senior Engineer – AI Workflows

• Define and execute the technical strategy and roadmap for cloud and platform security engineering
• Architect scalable, end-to-end security data platforms supporting telemetry, risk analytics, compliance, and vulnerability management
• Establish engineering standards, secure-by-design principles, and best practices for data and platform security
• Translate complex, ambiguous security and business challenges into actionable, scalable technical solutions
• Lead solution architecture design, including threat modeling and alignment with enterprise security and regulatory requirements
• Oversee development and operation of cloud-based platforms processing high-volume security data (logs, alerts, audit trails)
• Ensure platform reliability through strong data quality, observability, governance, and performance management (SLA/SLO/KPI-driven)
• Drive automation of security controls, monitoring, and incident response processes
• Lead and develop engineering teams while partnering with cross-functional stakeholders to deliver secure, high-impact outcomes

AI Enablement Engineering Tech Lead

• Implement new product features &amp; support/ extend existing ones
• Build frontend platform components leveraged across our internal, external products and ecommerce storefronts like Shopify, Salesforce etc
• Work with product and design teams toTranslate product and design work (Figma) into robust, accessible, high-performance UI components&nbsp;
• Own frontend architecture decisions and uphold code-quality standards alongside other senior engineers, and mentor teammates through code review
• Contribute to development and maintenance of backend services using primarily Golang, Node.js
• Understanding of google cloud platform (GCP), CI/CD and be able to deploy code and infrastructure components
• Work with datastores like PostgreSQL, Cloud Spanner, BigQuery, and Bigtable
• Understand and implement RESTful or GraphQL APIs

Senior Fullstack Developer – AI

• Lead discovery with senior stakeholders to identify high-impact agentic use cases
• Translate complex business problems into production-ready AI system designs
• Architect secure, auditable agentic workflows (reasoning, tool use, data access, evaluation)
• Drive solutions from prototype to AltaForge production deployment (our internal agentic platform)
• Ensure all solutions align with AltaForge architecture for reusability and scale
• Define and enforce evaluation standards to measure outcomes and value creation
• Support account growth by ensuring successful delivery and lifecycle value realization

Senior AI Architect

AI Research Scientist

• Develop automated calibration workflows.
• Monitor system performance and operational drift.
• Improve reliability and recovery processes.
• Analyze large experimental and operational datasets.
• Develop machine learning models to optimize performance.
• Generate insights to support engineering teams.
• Collaborate with hardware, software, and operations teams.
• Support deployment of automated operational tools.

Calibration, AI-Ops Scientist

• Deploy and monitor ML systems in production.
• Own the evaluation stack - golden datasets, "model-as-a-judge" frameworks, inter-annotator agreement, and regression tests that gate releases.
• Build and maintain our vector embeddings ecosystem and the retrieval, classification, and similarity patterns that sit on top of it.
• Partner with Data Science on annotation workflows, PII scrubbing, and ground-truth pipelines.
• Improve our MLOps foundations - versioning, observability, drift detection.
• Translate fuzzy product problems into measurable AI features with clear success criteria.

ML Engineer

• Bring novel ideas and the content of scientific papers into high-performing and robust scientific code.
• Lead the ideation, benchmarking, and execution of complex datasets and ML models, ensuring seamless integration into large-scale simulation frameworks.
• Drive software through the entire product lifecycle—from foundational research and implementation to launch and long-term support—ensuring technical excellence at every stage.

ML Research Engineer, AI for Life Sciences

• Design, develop and deploy interactive dashboards and reports using Power BI. (≈ 50% of the time)
• Design, train and validate predictive models (ML, etc.). (≈ 20% of the time)
• Extract and manipulate data from a variety of sources.
• Ensure delivery and continuous development of our BI applications to meet business needs.
• Provide support and expertise to our clients on optimal use of Power BI.
• Produce detailed monthly reports to track departmental performance and support decision-making.
• When required, provide reports and recommendations to company leadership.
• Proactively identify improvements to reporting and visualization processes.

Data Analyst – Machine Learning

• Design, train, and deploy scalable machine learning models for critical FinTech applications, including credit risk assessment, fraud detection, and personalized financial recommendations, using frameworks like PyTorch and LightGBM.
• Architect ML pipelines integrating with backend systems to process high-throughput data streams with low-latency inference for real-time decision-making.
• Leverage AI tools to automate experimentation, hyperparameter tuning, and test-driven ML development, accelerating the delivery of robust, production-ready models.
• Support the full ML lifecycle, including feature engineering, model evaluation, A/B testing, monitoring for drift, and seamless scaling to support explosive user growth while ensuring compliance with financial regulations.
• Experiment with advanced techniques in deep learning and reinforcement learning to push the boundaries of what's possible in consumer finance.

Machine Learning Engineering, Intern

• Lead the design and implementation of complex ML/AI systems end-to-end, owning architecture decisions and driving solutions from research through production at scale
• Build and evolve scalable ML platforms, pipelines, and infrastructure that support reliable, repeatable model development and deployment across teams
• Set the standard for AI-forward engineering, using tools like Claude and Cursor with sophistication and helping the team adopt them effectively
• Partner with product, engineering, and leadership to shape AI strategy and align technical direction with business outcomes
• Translate complex AI tradeoffs, risks, and opportunities into clear narratives that drive decision-making across technical and non-technical stakeholders
• Lead design reviews and technical discussions, raising the bar for engineering rigor and constructive challenge across the team
• Define AI architecture and engineering standards, bringing depth on tradeoffs, long-term implications, and responsible AI practices
• Mentor and grow junior and mid-level engineers, multiplying impact through coaching, code reviews, and pairing on hard problems
• Take ownership of the most ambiguous and highest-stakes pieces of work, driving them through to production with care for reliability, cost, and safety.

Staff AI Engineer

• Explore and apply frontier AI-enabled development tools and techniques in your daily work, and help assess where they add real value.
• Contribute to transforming the FME Flow codebase to support AI-assisted development — improving structure, documentation, and test coverage so AI tooling and engineers are effective.
• Work closely with the Principal Software Developer for AI Enablement to learn best practices, and help share and embed them within the Flow development team.
• Deliver reliable, maintainable code changes with growing independence, and troubleshoot and resolve issues with persistence.
• Participate in sprint and project planning, helping to anticipate risks and dependencies for your work.
• Collaborate across QA, UX, and Product, support more junior developers and interns, and contribute ideas that improve quality, workflow, and how the team uses AI.

Software Developer II, AI Enablement

• Design, develop, and operate large-scale, high-performance infrastructure that powers Confluent Cloud.
• Build foundational software to improve reliability, scalability, and efficiency across cloud environments.
• Work on distributed systems challenges such as consensus algorithms, failover strategies, and resource allocation.
• Collaborate with teams across Confluent to optimize and enhance infrastructure for real-time data streaming use cases.
• Troubleshoot and improve system reliability, observability, and performance across multiple cloud providers (AWS, Azure, GCP).

Senior Software Engineer – Streaming AI

• Work on Marketing Website and assist in developing new user-facing features using HTML, CSS, JavaScript and modern front-end frameworks
• Leverage AI coding assistants (e.g., GitHub Copilot, Claude, ChatGPT) to accelerate development, workflows and improve code quality
• Integrate AI-powered features into web interfaces, such as chatbots, personalized content recommendations, or AI-driven search functionality
• Refactor and write clean, reusable, and maintainable code with HTML, CSS, and JavaScript
• Work closely with design and backend teams to implement intuitive UI/UX designs, including AI-enhanced user experiences
• Experiment with prompt engineering to optimize AI tool outputs for development tasks
• Optimize components for speed, accessibility, and cross-browser compatibility
• Participate in team code reviews and agile development workflows
• Document AI workflows and best practices for the team

AI Developer, Intern/Co-op

• Collaborate on projects at the intersection of research and product with a diverse, global team of researchers and engineers
• Support research through the construction of ML pipelines, prototypes, and reusable, testable code
• Process data and analyze feature extractions
• Analyze errors and provide solutions to problems
• Present results to collaborators and leadership

Machine Learning Engineer – 3D Geometry, Multi-Modal

• Reporting to the Director, AI & Digital Planning, the Senior AI Engineer will be responsible for using the latest AI tools to design, prototype, build, evaluate, and scale AI solutions including Generative and Agentic AI
• Define and document architectural patterns, technical standards, and design decisions
• Collaborate with internal teams to develop AI solutions
• Embed directly within clinical and operational teams to identify automation and agentic opportunities
• Manage the end-to-end lifecycle of AI solutions from design and development to implementation and sustainability
• Lead AI integration and implementation efforts within the hospital, and with partners
• Develop API endpoints and implement Model Context Protocols (MCPs) for exchanging healthcare information
• Connect AI agents to core hospital systems via APIs
• Design and implement techniques to optimize AI performance
• Build reliability and safety infrastructure for AI systems in a healthcare environment
• Ensure AI solutions comply with privacy, security, and responsible AI requirements
• Monitor and troubleshoot production issues

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