Senior ML Platform / ML Infrastructure Engineer II (AI)

TL;DR

Senior ML Platform / ML Infrastructure Engineer (AI): Designing and building standardized ML training and serving pipelines with an accent on real-time inference, infrastructure as code, and model lifecycle governance. Focus on implementing ultra-low-latency serving patterns, ensuring end-to-end observability, and collaborating on cost-efficient, secure architectures.

Location: Hybrid in Toronto or Montreal, Canada (2 days/week in-office).

Company

hirify.global is the #1 loyalty app for mobile gamers, helping them discover new games and earn rewards.

What you will do

• Design, build, and operate standardized ML training-to-serving pipelines using Airflow.
• Manage real-time and batch inference on AWS SageMaker, including multi-model endpoints and autoscaling.
• Implement ultra-low-latency serving patterns with Redis/Valkey for feature caching and online retrieval.
• Provision and manage ML/data infrastructure with Terraform, focusing on SageMaker, ECR/ECS/EKS, and network resources.
• Establish and manage model lifecycle governance with registries, approval workflows, and audit trails.
• Implement end-to-end observability for ML workflows, including data freshness, drift checks, and performance SLOs.

Requirements

• 5+ years building and operating production-grade ML/data platforms with a focus on serving, reliability, and developer experience.
• Strong software engineering skills in Python, Go, or Java for building resilient services and APIs.
• Deep experience with AWS SageMaker inference: endpoint configuration, containerization, autoscaling.
• Expertise with online feature stores like Redis/Valkey in ML serving contexts.
• Proven Terraform experience for end-to-end ML/data infrastructure management.
• Extensive experience with Airflow orchestration at scale: dependency modeling, DAG factories, and integrations.

Culture & Benefits

• Welcoming and fun work environment with team lunches, game nights, and company-wide events.
• Culture deeply rooted in growth, supported by a smart, dynamic, and enthusiastic team.
• Utilizes data to constantly learn, improve, and adapt.
• Fosters an environment where ideas are shared, boundaries are pushed, and calculated risks are encouraged.