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Staff MLOps Engineer
Neo Financial — топовый работодатель в Канаде с отличной репутацией и быстрым ростом. Предложение включает опционы и участие в прибыли, что делает позицию очень привлекательной для опытных специалистов.
Сложность вакансии
Роль уровня Staff требует не только глубоких технических знаний в MLOps (AWS, Terraform, Ray), но и лидерских качеств для менторства и стратегического развития платформы. Высокая планка ответственности в быстрорастущем финтехе и работа в офисе добавляют сложности.
Анализ зарплаты
Для позиции Staff MLOps Engineer в Калгари рыночный диапазон составляет 160,000–210,000 CAD в год. Учитывая статус Neo как быстрорастущего финтеха, общая компенсация (включая бонусы и опционы) может быть выше среднего по рынку.
Сопроводительное письмо
I am writing to express my strong interest in the Staff MLOps Engineer position at Neo Financial. With over 7 years of experience in software engineering and a deep focus on productionizing machine learning systems, I have consistently delivered scalable infrastructure that bridges the gap between data science research and reliable production services. My expertise in AWS, Terraform, and managing complex pipelines for both tree-based models and neural networks aligns perfectly with Neo's mission to build a world-class financial platform.
In my previous roles, I have successfully led the migration of ML compute platforms and implemented comprehensive observability using Datadog, which significantly reduced model drift and improved system reliability. I am particularly excited about Neo's commitment to AI-powered development and the opportunity to mentor a high-performing team while shaping the evolution of your ML infrastructure. I am eager to bring my technical leadership and passion for innovation to a company that has been recognized as the fastest-growing tech startup in Canada.
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Присоединяйтесь к лидеру финтеха Канады и создавайте будущее MLOps платформ в Neo Financial!
Описание вакансии
*Join us to build a more rewarding financial future for all Canadians*
At Neo, we’re on a mission to build a more rewarding financial future for all Canadians. Life at a rapidly scaling tech startup isn’t for everyone. It’s complex, fast-paced, high-pressure, but also incredibly fulfilling. Since its founding in 2019, Neo has gained incredible traction and is one of the fastest-growing fintech companies in Canada.
- #1 on Deloitte’s Technology Fast 50 for 2023, 2024, and 2025 — the first company ever to achieve a three-peat at the top!
- #1 Fastest Growing Company in Canada for 2024 by Globe & Mail
- Top-ranked mobile apps and credit cards
- Team of 500+ people
- 1M+ customers
- 10K+ retail partners
About the Role
We're looking for a Staff MLOps Engineer to lead the design, delivery, and evolution of our machine learning platform. In this role you will own the infrastructure and tooling that takes models from experimentation to production — building reliable, observable, and scalable ML systems on AWS. You'll work closely with data scientists to productionize models, mentor junior engineers, and champion modern AI-powered development practices across the team.
This is a high-impact, senior individual contributor role where you'll shape how we build, deploy, and monitor machine learning systems powering financial products used by millions of Canadians.
What You'll Do
- Design and maintain batch and real-time ML inference pipelines for both tree-based models (XGBoost, LightGBM, CatBoost) and neural networks (PyTorch, TensorFlow)
- Build and manage CI/CD pipelines for ML model deployment
- Own infrastructure-as-code (Terraform) for the ML platform across cloud services
- Implement observability for ML systems — logging, tracing, metrics, drift detection, and alerting
- Manage experiment tracking and model registry platforms and automate the path from training to staging to production
- Build and operate feature pipelines and data platform integrations
- Lead migration to next-generation ML compute platforms (e.g., Anyscale), modernizing distributed training and serving infrastructure
- Partner with data scientists to productionize models — translating research prototypes into reliable, monitored services
- Mentor junior engineers through code reviews, pair programming, and technical guidance; establish best practices and learning paths that raise the bar for the team
- Champion AI-powered development tools (e.g., Cursor, Claude Code) — drive adoption across the team, integrate AI into daily workflows and CI/CD, and evangelize effective usage
What You Bring
Required
- 7+ years in software engineering, MLOps, or platform engineering, with 3+ years focused on production ML systems
- Deep cloud experience (ideally AWS)
- Infrastructure-as-code with Terraform
- Production ML pipeline experience across both tree-based models (XGBoost, LightGBM) and neural networks (PyTorch, TensorFlow), using platforms such as Anyscale, SageMaker, or similar
- ML experiment tracking and model registry tools (e.g., MLflow, Weights & Biases)
- Observability for ML systems using Datadog (logs, APM, metrics, anomaly detection)
- CI/CD with GitHub Actions and AWS CodePipeline
- Fluency in Python and SQL; familiarity with data platforms (e.g., Snowflake, Databricks)
- Proven track record mentoring and upleveling junior engineers
- Experience with AI-powered development tools (e.g., Cursor, Claude Code) and a drive to integrate AI into engineering workflows
Nice to Have
- Experience with distributed training and Ray-based compute frameworks
- Background in financial services, fintech, or other regulated industries
- Contributions to open-source MLOps or infrastructure tooling
- Familiarity with model governance, audit trails, and compliance requirements for ML in production
What does life as an Owner at Neo Include?
🇨🇦 You will redefine the financial landscape with top talent in Canada to help everyday Canadians win, and grow quickly while doing it
📈 Ownership structure that enables you to share in our path to victory
🏅Earn BIG with our short term incentive plans (STIP)
👥 Collaborating with the brightest minds to build something meaningful, united by a shared commitment to innovation, curiosity, and excellence
❤️🩹 Flexible health benefits & life insurance
🌴 21 Days PTO + stat holidays, and personal leave
💪 Health & personal spending accounts
🤑 A wide variety of discounts through our partner network
🏠 Access to early use of products and discounts on fixed-term Neo mortgages
👶 Parental top-up & equity vesting during long term leave
🎉 Neo night events
🧠 Company wide huddles, and education
Working at Neo:
The people who thrive at Neo are resourceful, growth-oriented, and driven to win. We hold ourselves to high standards because we’re on a mission that matters: to build a more rewarding financial future for all Canadians. As individuals and as a team, we constantly challenge ourselves and each other to raise the bar and deliver exceptional experiences for our customers. Our commitment to customer success drives everything we do, from building world-class products to providing exceptional support at every step of their journey. If this resonates with you, keep reading.
We trust, respect, and support each other. This means honest conversations, constructive input, and empowering each other to move fast and think big. You’ll be surrounded by people who push you to be your best. We primarily collaborate in person across our Calgary, Winnipeg, and Toronto offices, where ideas move quickly and teams build momentum together. As we continue to scale and evolve, we’re looking for builders, innovators, and problem solvers who thrive on challenge–people who would rather blaze a trail through uncertainty than follow a well-paved path.
All team members have a stake in Neo’s success and earn meaningful equity through stock options. This ownership mindset is at the heart of everything we do — when Neo grows, we all grow. If you’ve heard that Neo employees work hard, it’s true. We hold ourselves accountable for delivering on our commitments to our customers, partners, and each other. Working at Neo means taking ownership of your work and driving results, knowing that your contributions directly impact the company’s success.
Apply with us:
We believe in equal opportunity and are committed to creating an inclusive climate where everyone can thrive. Customers trust us with their finances, so successful candidates for this position will be required to undergo a security screening, including a criminal records check and a credit check.
By continuing with your application, you agree to the Candidate Privacy Notice, which guides how we process your personal information for the purpose of your application.
Neo Financial leverages artificial intelligence (AI) to reinforce our candidate evaluation process, including the initial assessment of applications and screening of candidates. These tools support our recruitment team; however, all final hiring decisions are made by humans and are not based exclusively on automated processing. If you require further information regarding our data processing practices, please contact us.
We are hiring for an open, vacant position.
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Навыки
- AWS
- Python
- Terraform
- PyTorch
- SQL
- GitHub Actions
- CI/CD
- MLOps
- Snowflake
- Databricks
- TensorFlow
- XGBoost
- Ray
- MLflow
- LightGBM
- Weights & Biases
- Datadog
- CatBoost
- Anyscale
Возможные вопросы на собеседовании
Для роли Staff важно понимать, как кандидат проектирует системы с нуля и обеспечивает их масштабируемость.
Опишите архитектуру MLOps платформы, которую вы бы спроектировали для обработки как пакетных, так и real-time предсказаний в условиях финтеха.
Вакансия упоминает переход на Anyscale и Ray; важно проверить опыт работы с распределенными вычислениями.
С какими основными сложностями вы сталкивались при масштабировании распределенного обучения моделей и как их решали?
В финтехе критически важна точность и отслеживание изменений.
Как вы организуете процесс мониторинга дрейфа данных и моделей (drift detection) для критически важных финансовых сервисов?
Роль подразумевает внедрение инструментов вроде Cursor и Claude Code в рабочие процессы команды.
Каков ваш подход к интеграции AI-инструментов разработки в CI/CD пайплайны для повышения продуктивности всей инженерной команды?
Staff-инженер должен уметь разрешать конфликты и направлять команду.
Расскажите о случае, когда вам пришлось убеждать команду или руководство принять непопулярное техническое решение в области инфраструктуры.
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