Role overview

Benevity is a social impact platform that enables companies and their employees to drive positive change through giving, volunteering, grantmaking and other initiatives. As a Senior AI Systems Engineer, you will design, build and operate the machine learning and generative AI systems that power Benevity’s products, transforming prototypes into reliable production features that help impact leaders maximize their good work.

Key responsibilities

Design and operate end-to-end machine learning and generative AI features, encompassing data ingestion, feature pipelines, model serving and monitoring across production environments.
Build large language model-powered applications including search, assistants and agents, with a focus on retrieval-augmented generation pipelines, tool integrations and multi-step workflows.
Develop evaluation frameworks for generative AI systems that combine offline benchmarks, online metrics and human review processes.
Implement safety measures for generative AI applications, addressing prompt injection defenses, hallucination mitigation and responsible AI practices.
Apply MLOps standards including model versioning, automated testing, A/B testing, drift detection and safe rollback procedures.
Translate product requirements and business problems into clear technical specifications and delivery plans in collaboration with product managers and data scientists.
Mentor mid-level engineers on production machine learning practices and provide thoughtful code reviews during design reviews.

Requirements

Bachelor’s or Master’s degree in Computer Science, Mathematics or a related field, or equivalent professional experience demonstrating comparable expertise.
At least five years of software engineering experience, with a minimum of three years actively building and deploying machine learning models in production environments.
Hands-on proficiency across the full machine learning lifecycle including training, evaluation, deployment, monitoring and model retraining.
Strong Python programming skills and demonstrated experience with machine learning frameworks such as PyTorch, TensorFlow or scikit-learn.
Practical experience with at least one major cloud platform (AWS, Azure or Google Cloud) and with distributed computing workloads.
Working knowledge of data pipelines, feature engineering and large-scale data processing techniques.
Direct experience shipping large language model-based features, including prompt engineering, few-shot techniques and tool or function calling capabilities.
Experience building retrieval-augmented generation systems involving embeddings, vector databases and retrieval optimization.
Familiarity with machine learning tooling such as experiment tracking, model registries, feature stores or orchestration frameworks.
Experience implementing CI/CD pipelines for machine learning with automated validation and rollback strategies.
Understanding of data governance, privacy considerations, bias mitigation and explainability in artificial intelligence systems.
Strong communication skills and a collaborative approach when working with both technical and non-technical partners.

What makes this role worth considering

You’ll work within Benevity’s collaborative culture, partnering closely with product managers, data scientists, design and engineering teams in an outcome-driven environment. The company operates a flexible hybrid model with offices across multiple locations including Toronto, and emphasizes diversity, equity, inclusion and belonging as core values embedded throughout the organization.