Staff AI Developer

Accountabilities

AI System Design and Development: Design, develop, and maintain production-grade AI systems across machine learning pipelines, generative AI applications, and agentic workflows, ensuring reliability, scalability, and operational effectiveness.
Data Integration and Intelligence: Translate ambiguous business and technical challenges into practical solutions that combine heterogeneous data sources using rule-based, probabilistic, and machine learning approaches.
Generative AI and Agentic Systems: Develop and optimize LLM-powered applications, evaluate emerging orchestration frameworks, explore fine-tuning approaches, and implement appropriate evaluation methods and safeguards.
Machine Learning Infrastructure: Build and maintain data and ML pipelines for model training, deployment, monitoring, and continuous improvement within cloud-native environments, preferably using AWS.
Security and Reliability: Apply security-first engineering principles, observability, infrastructure-as-code, CI/CD, and operational best practices to ensure AI solutions remain secure, maintainable, and scalable.
Cross-Functional Collaboration: Partner with data scientists, engineering managers, product teams, threat researchers, and security operations analysts to translate operational needs into effective technical solutions and measurable customer outcomes.
Technical Communication: Communicate implementation progress, architectural decisions, risks, and trade-offs clearly to technical and non-technical stakeholders. Produce design documents, architecture decision records, and actionable status updates.
Hands-On Engineering: Take ownership of daily engineering activities, including coding, code reviews, debugging production issues, testing, and resolving technical blockers.
Mentorship and Quality Improvement: Mentor mid-level and early-career engineers while raising standards for code quality, testing, documentation, and operational practices.
AI Evaluation and Feedback: Instrument AI systems to measure quality and performance, collaborate with operational teams to establish feedback loops, and continuously improve model outputs and customer experiences.
Requirements
Professional Experience: At least six years of experience building intelligent systems, distributed platforms, or AI-enhanced applications.
AI Engineering Expertise: Demonstrated experience designing and delivering production systems that support machine learning, data science, generative AI, or agentic workloads.
Generative AI: Practical experience deploying generative AI applications, including prompt engineering, model evaluation, guardrails, and some exposure to fine-tuning.
Agentic Frameworks and LLM Integration: Familiarity with agentic frameworks and LLM integration patterns, such as Amazon Bedrock, AgentCore, LangGraph, or equivalent technologies.
Programming and Software Engineering: Strong hands-on coding skills and the ability to design, implement, test, and maintain robust software systems.
Cloud and DevOps: Experience with cloud-native data services, preferably AWS, as well as infrastructure-as-code, CI/CD practices, and secure software development principles.
Cross-Functional Delivery: A track record of collaborating with data science, product, engineering, and operational teams to deliver customer-facing solutions.
Communication Skills: Excellent written and verbal communication skills, including the ability to explain technical concepts, document architectural decisions, and communicate trade-offs effectively.
Cybersecurity Knowledge: Familiarity with cybersecurity concepts such as threat detection, alert triage, risk modeling, exposure management, telemetry, or the MITRE ATT&CK framework.
Data Engineering: Knowledge of data processing and streaming technologies such as Spark, Flink, Kafka, or Databricks is an asset.
AI Quality Measurement: Familiarity with AI evaluation frameworks, including LLM-as-a-judge techniques and human-in-the-loop evaluation, is beneficial.
Security-First Mindset: Understanding of DevSecOps practices and the importance of protecting sensitive data throughout the AI development lifecycle.
Leadership and Mentorship: Ability to guide teammates, influence technical decisions, and contribute to engineering excellence while remaining actively involved in implementation.
Additional Conditions: Candidates must be prepared to complete the required background checks and follow applicable information security policies. Remote video interviews are expected to be conducted with cameras on, subject to reasonable accommodations when necessary.
Benefits
Competitive Compensation: Annual base salary ranging from CAD 75,000 to CAD 246,000, depending on role level, skills, experience, and location.
Performance Incentives: Eligibility for variable incentive compensation in addition to base salary.
Equity Participation: New-hire equity grants and employee equity opportunities, subject to applicable terms.
Retirement Savings: Canadian Registered Retirement Savings Plan (RRSP) matching.
Comprehensive Health Coverage: Private benefits covering medical, dental, disability, life, accidental death and dismemberment (AD&D), and additional services.
Mental Health and Wellbeing: Employee Assistance Program (EAP) with mental health support and related resources.
Flexible Time Off: Flexible paid time off to support work-life balance.
Paid Volunteer Days: Opportunities to contribute to community initiatives through paid volunteer time.
Family Support: Paid parental leave and fertility support.
Learning and Development: Training programs, professional development resources, and opportunities to deepen technical expertise.
Remote Work: Opportunity to work remotely from Canada.
Inclusive Culture: A collaborative environment that values diverse perspectives, accessibility, employee wellbeing, and meaningful contributions to cybersecurity.