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Senior AI Systems Engineer

  • Remote, Canada

Remote

$82,900 to $113,960 CAD / year · Full-time

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Job details

Pay
$82,900 to $113,960 CAD / year
Job type
Full-time
Workplace
Remote
Remote eligibility
Canada listed. Check the posting for province restrictions.
Experience
5+ years of software engineering experience, including 3+ years building and deploying ML models in production.
Posting language
English
Posted

Original job description

Meet Benevity

Benevity is the way the world does good, providing companies (and their employees) with technology to take social action on the issues they care about. Through giving, volunteering, grantmaking, employee resource groups and micro-actions, we help most of the Fortune 100 brands build better cultures and use their power for good. We’re also one of the first B Corporations in Canada, meaning we’re as committed to purpose as we are to profits. We have people working all over the world, including Canada, Spain, Switzerland, the United Kingdom, the United States and more!

Benevity’s software architecture has evolved to include a diverse technology stack. The front-end application, using mainly VueJS, is designed for both desktop and mobile web rendering. Our back-end systems (some Java SpringBoot, some PHP) manage data processing, interfaces with external providers, and ensures robust security. We run and operate our systems in both GCP and AWS cloud, leveraging where possible cloud-native technology. We emphasize clean, maintainable code and use GIT for version control and collaboration. Additionally, our platform integrates with various external services for functionalities like email communication, content storage, and server-to-server interactions.

Our culture is driven by our core value of “we-are-we” and as a Senior AI Systems Engineer, you will work in an outcome-driven environment where collaboration with your product, design and engineering counterparts is paramount.

If you’re eager to make a difference and thrive in a collaborative setting, we invite you to join our team!

Position Overview:

As a Senior AI Systems Engineer, you'll build and run the machine learning and GenAI systems that power Benevity's products. You'll turn promising prototypes into reliable, production-grade features that help impact leaders do more good. You'll own significant systems from design through monitoring, work closely with data scientists and product managers, and help raise the bar for the engineers around you. You'll work closely with the Staff engineers on architecture and strategy, and lead delivery in your own area.

What you’ll do:

  • Design, build and operate ML and GenAI features end to end, from data ingestion and feature pipelines to model serving and monitoring.

  • Build LLM-powered applications such as search, assistants and agents, including RAG pipelines, tool integrations and multi-step workflows.

  • Create evaluation frameworks for GenAI systems that combine offline benchmarks, online metrics and human review.

  • Implement guardrails for GenAI applications, including prompt injection defenses, hallucination mitigation and responsible AI practices.

  • Put MLOps standards into practice: versioning, automated testing, A/B testing, drift detection and safe rollbacks.

  • Translate business problems from product managers and data scientists into clear technical requirements and scoped delivery plans.

  • Write design docs, take part in design reviews and give thoughtful code reviews.

  • Mentor mid-level engineers on production ML practices and system design.

  • Balance latency, cost and quality when choosing models, prompts and infrastructure.

  • Find and fix bottlenecks in pipelines, serving and platform performance.

What you’ll bring:

  • Bachelor's or Master's degree in Computer Science, Mathematics or a related field, or equivalent professional experience.

  • 5+ years of software engineering experience, including 3+ years building and deploying ML models in production.

  • Hands-on experience across the ML lifecycle: training, evaluation, deployment, monitoring and retraining.

  • Strong Python skills and experience with ML frameworks such as PyTorch, TensorFlow or scikit-learn.

  • Experience with at least one major cloud platform (AWS, Azure or Google Cloud) and with distributed workloads.

  • Working knowledge of data pipelines, feature engineering and large-scale data processing.

  • Familiarity with ML tooling such as experiment tracking, model registries, feature stores or orchestration frameworks.

  • Experience with CI/CD for ML, including automated validation and rollback strategies.

  • Experience shipping LLM-based features, including prompt engineering, few-shot techniques and tool or function calling.

  • Experience building RAG systems, including embeddings, vector databases and retrieval tuning.

  • Exposure to agentic workflows and orchestration frameworks such as LangChain, LlamaIndex or ADK.

  • Solid grasp of APIs, microservices and reliability fundamentals.

  • Awareness of data governance, privacy, bias and explainability in AI systems.

  • Clear communication and a collaborative approach with technical and non-technical partners.

Great-to-haves:

  • Certification in relevant cloud platforms or technologies.

Discover your purpose at work

We’re not employees, we’re Benevity-ites. From all locations, backgrounds and walks of life, who deserve more …

Innovative work. Growth opportunities. Caring co-workers. And a chance to do work that fills us with a sense of purpose.

If the idea of working on tech that helps people do good in the world lights you up ... If you want a career where you’re valued for who you are and challenged to see who you can become …

It’s time to join Benevity. We’re so excited to meet you.

Where We Work

At Benevity, we embrace a flexible hybrid approach to where we work that empowers our people in a way that supports great work, strong relationships, and personal well-being. For those located near one of our offices, while there’s no set requirement for in-office time, we do value the moments when coming together in person helps us build connection and collaboration. Whether it’s for onboarding, project work, or a chance to align and bond as a team, we trust our people to make thoughtful decisions about when showing up in person matters most.

Join a company where DEIB isn’t a buzzword

Diversity, equity, inclusion and belonging are part of Benevity’s DNA. You’ll see the impact of our massive investment in DEIB daily - from our well-supported employee resources groups to the exceptional diversity on our leadership and tech teams.

We know that diverse backgrounds, experiences, skills and passions are what move our business and our people forward, so we're committed to creating a culture of belonging with equal opportunities for everyone to shine.

That starts with a fair and accessible hiring process. If you want to feel seen, heard and celebrated, you belong at Benevity.

Candidates with disabilities who may require accommodations throughout the hiring or assessment process are encouraged to reach out to accommodations@benevity.com.

More about this job

Responsibilities

You will design, build, and operate end-to-end machine learning and GenAI features, including LLM-powered applications and RAG pipelines. Additionally, you will implement MLOps standards, create evaluation frameworks, and mentor mid-level engineers on production ML practices.

Requirements

Candidates must have a Bachelor's or Master's degree in a relevant field and at least 5 years of software engineering experience, with 3+ years specifically in ML production. Strong proficiency in Python, ML frameworks, and cloud platforms is required, along with experience in LLM-based feature development.

Skills

  • Python
  • Machine Learning
  • GenAI
  • LLM
  • MLOps
  • PyTorch
  • TensorFlow
  • Scikit-learn
  • AWS
  • Google Cloud
  • RAG
  • LangChain
  • LlamaIndex
  • System Design
  • Data Pipelines
  • CI/CD

Visa sponsorship

Not detected in the job text

Categories

  • Technology
  • Software
  • Data & Analytics
  • Engineering

Keywords

  • Machine Learning
  • GenAI
  • LLM
  • Python
  • PyTorch
  • TensorFlow
  • Scikit-learn
  • AWS
  • Google Cloud
  • MLOps
  • RAG
  • LangChain
  • LlamaIndex
  • Data Pipelines
  • CI/CD
  • System Design
  • Prompt Engineering
  • Vector Databases
  • Microservices
  • API
  • Software Engineering
  • Artificial Intelligence
  • Feature Engineering
  • Model Serving
  • Drift Detection
  • A/B Testing
  • Cloud-native
  • Distributed Workloads
  • Data Governance
  • Responsible AI

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