4 days ago
Toronto, CanadaStaff+
Responsibilities
- Design and oversee robust end-to-end ML architecture from data ingestion and feature stores through model serving and monitoring.
- Define the long-term roadmap for AI infrastructure and orchestration frameworks.
- Build extensible, self-service AI platform primitives including APIs, SDKs, and standardized patterns.
- Establish MLOps standards covering testing, maintainability, scalability, model versioning, A/B testing, drift detection, and automated rollbacks.
- Architect LLM-powered applications such as copilots, search, assistants, and agents, including RAG pipelines, tool integrations, and multi-step reasoning workflows.
- Develop evaluation frameworks using offline benchmarks, online metrics, and human feedback.
- Drive prompt engineering, agent design, orchestration, safety guardrails, prompt-injection defenses, hallucination mitigation, and responsible AI practices.
- Lead architecture reviews, mentor engineers, and collaborate with Product Managers, Data Scientists, and ML Engineers.
- Align AI architecture with security policies, industry regulations, and best practices.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field, or equivalent deep professional experience.
- At least 8 years of software engineering experience, including at least 4 years architecting and deploying ML models in production at scale.
- At least 2 years in a technical leadership role involving scalable platforms, technology transformation, or modernization initiatives.
- Proven Staff- or Senior-level experience with technical leadership, architecture ownership, and engineering mentorship.
- Experience building internal platforms and self-service infrastructure for engineering teams.
- Deep expertise in MLOps and production ML systems, including training, evaluation, deployment, monitoring, and lifecycle management.
- Strong experience with AWS or Google Cloud for scalable, distributed AI and ML workloads.
- Experience with data architecture, data engineering, pipelines, feature engineering, data modeling, and large-scale processing.
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Experience with CI/CD and ML deployment pipelines, automated testing, validation, and rollback strategies.
- Experience with LLM systems, GenAI applications, prompt engineering, RAG, embedding strategies, vector databases, agentic workflows, and orchestration frameworks.
- Strong system design knowledge covering reliability, scalability, distributed systems, APIs, and microservices architecture.
- Knowledge of data governance, model governance, security, privacy, bias, explainability, and responsible AI.
- Excellent communication, collaboration, stakeholder influence, and cross-functional alignment skills.
- Relevant cloud-platform or technology certification is preferred.
Benefits
- Flexible hybrid work approach with no fixed in-office requirement for employees near an office.
- Opportunities for innovative work, professional growth, and purpose-driven technology development.
- Employee resource groups and a strong diversity, equity, inclusion, and belonging culture.
- Accessibility accommodations are available throughout the hiring and assessment process.
Categories
About Benevity
Benevity builds an enterprise SaaS platform for corporate social impact, covering employee giving, volunteering, grants management, ERGs, and donation processing with global charity vetting. Companies use it to run and measure programs for employees and customers, with integrations and payment rails. Founded in 2008 and headquartered in Calgary, it is privately held and part of Hg’s portfolio, serving many large, multinational brands.