Lead, Data Development, AI Platform
New
CanadaFull-TimeLead
Salary98,400 - 137,800 CAD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- Data engineeringRESTful APIsSoftware EngineeringLLM
Requirements
- 8+ years of experience in software engineering, data engineering, AI platform engineering, or related technical fields.
- Experience leading or mentoring engineering teams, including setting technical direction, supporting professional growth, and driving delivery accountability.
- Strong programming experience across backend services, APIs, data pipelines, or platform infrastructure.
- Proven experience designing and operating scalable systems, distributed architectures, workflow orchestration, or complex integrations.
- Experience building orchestration solutions involving multiple systems, automated workflows, query execution, or agent-based processes.
- Working knowledge of AI and LLM-based systems, including agents, tools, context interfaces, and orchestration layers.
- Experience developing data pipelines for AI or programmatic use cases with strong focus on quality, testing, observability, and maintainability.
- Familiarity with production reliability practices, including monitoring, debugging, security integration, performance optimization, and incident management.
- Strong ability to influence technical decisions through clear communication, tradeoff analysis, and compelling recommendations.
- Inclusive leadership mindset with experience building collaborative and diverse engineering teams.
- Strong planning and organizational skills, with the ability to translate long-term technical vision into actionable execution plans.
Responsibilities
- Lead the design and delivery of orchestration systems connecting AI analytics platforms, including query execution, federated data access, workflow automation, and multi-step agent processes.
- Define technical recommendations for platform architecture evolution, align solutions with leadership expectations, and guide execution through structured engineering practices.
- Own the reliability, scalability, observability, and quality standards of critical platform components, including routing infrastructure, agent infrastructure, and supporting services.
- Drive incident response, troubleshooting, root-cause analysis, and continuous improvement initiatives to maintain production-grade systems.
- Establish standards for data pipelines designed for AI and programmatic consumption, ensuring data quality, documentation, testing, monitoring, and operational reliability.
- Build and improve internal tools and automation that enhance AI engineering workflows, including deployment, monitoring, diagnostics, and operational support.
- Provide hands-on technical leadership through coding, architecture discussions, design reviews, and engineering best practices.
- Mentor engineers through coaching, feedback, code reviews, and career development support.
- Collaborate with AI, data architecture, and engineering teams to ensure seamless integration between platform services, data models, context layers, and AI workflows.
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