Engineering Manager (Ai)

TL;DR

Engineering Manager (AI): Shaping the future of ML Data Foundations platform, ML Serving and observability at hirify.global with an accent on defining long-term strategy and leading the team in building the next generation of serving infrastructure. Focus on aligning the vision and roadmap with business objectives and industry best practices.

Location: Toronto

Salary: CA$213,600 - CA$320,400

Company

hirify.global is a financial infrastructure platform for businesses that processes over $1.9T in payments volume per year, which is roughly 1.6% of the world's GDP.

What you will do

• Hire, lead and manage a team of talented engineers, providing mentorship and support.
• Collaborate with cross-functional teams to understand user needs and translate them into technical solutions.
• Define the vision and roadmap, aligning it with business objectives and industry best practices.
• Drive the execution of projects, overseeing the entire development lifecycle from planning to delivery.
• Foster a collaborative and inclusive work environment, promoting innovation and knowledge sharing.
• Stay up-to-date with emerging technologies and advancements in AI/ML to identify opportunities for innovation.

Requirements

• 10+ years of software development experience and 3+ years of engineering management experience building user-centric products.
• Proven track record of building and operating large-scale, highly available, low-latency systems.
• Experience working in highly cross-functional organizations.
• Ability to thrive on a high level of autonomy and responsibility.
• The desire to encourage a healthy, inclusive work environment that's both supportive and challenging.
• Clear and persuasive writing and in-person communication.

Nice to have

• Experience in building large-scale serving or data infrastructure for machine learning use cases.
• Familiarity with cloud services (e.g., AWS) and cloud-based AI/ML services (e.g., SageMaker, Bedrock, Databricks, OpenAI, etc.).
• Comfortable working with geographically distributed teams.
• Experience with applying machine learning to real-world problems.