Staff MLOps Engineer

Staff MLOps Engineer 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 https://www.newswire.ca/news-releases/neo-financial-achieves-first-ever-three-peat-win-as-number-one-on-deloitte-s-technology-fast-50-list-889692540.html — the first company ever to achieve a three-peat at the top! - #1 Fastest Growing Company in Canada for 2024 by Globe & Mail https://www.businesswire.com/news/home/20240927952651/en/Neo-Financial-Ranks-No.-1-on-The-Globe-and-Mail%E2%80%99s-2024-List-of-Canada%E2%80%99s-Top-Growing-Companies - 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 o ... (truncated, view full listing at source)