DevOps Engineer (Engineering Productivity & Tooling)

Point Digital Finance, Inc.

3d•CA$144,528 - CA$159,741•Remote

About The Position

Point is transforming homeownership through innovative financial products — and we're doing it with an engineering organization that's going AI-native. We're looking for a Senior DevOps Engineer to sit at the center of that transformation: building internal tooling to measure and accelerate engineering productivity, ensuring the availability and reliability of our business systems, and eliminating friction across our Secure SDLC.

Requirements

5+ years of experience in Platform Engineering, DevOps, or Cloud Engineering; Bachelor's or Master's in Computer Science, Engineering, or a related technical field
Demonstrated experience building internal developer productivity tools and metrics systems (e.g., DORA metrics, cycle time, deployment frequency, engineering throughput)
Strong proficiency in at least one programming language (Python, Go, TypeScript, or similar) for building automation and internal tooling
Practical experience using AI coding agents (Claude Code, Cursor, GitHub Copilot, or similar) as part of your own engineering workflow
Hands-on experience with AWS services including compute (ECS, EKS, EC2, Lambda), storage (S3), data (RDS, Redis), and governance and identity (Control Tower, IAM)
Hands-on experience building and maintaining CI/CD workflows using GitHub Actions
Solid experience with Infrastructure as Code using Terraform or similar tools (AWS CDK, CloudFormation)

Nice To Haves

Experience managing or evaluating LLM hosting platforms such as AWS Bedrock — including model selection, token cost optimization, and multi-model evaluation frameworks
Experience building tooling to support agentic or multi-agent AI workflows (session tracking, cost attribution, observability)

Responsibilities

Build and maintain internal developer productivity tooling — dashboards, metrics, and measurement frameworks that surface engineering throughput, cycle time, and quality signals
Instrument token consumption and model cost across agentic workflows (task type, epic, user) to provide visibility and support cost optimization as we scale AI coding agent usage
Establish availability monitoring and alerting across all business systems and internal applications, and develop SOPs for detecting and responding to disruptions
Identify and reduce friction across the Secure SDLC — streamlining SAST/DAST integration, compliance tooling, and security scanning so engineers can ship fast without cutting corners
Manage, scale, and improve AWS cloud infrastructure to support reliable and secure production systems
Build, operate, and continuously improve CI/CD pipelines using GitHub Actions to enable fast, safe, and repeatable deployments
Develop and maintain Infrastructure as Code (Terraform) for consistent, auditable, and scalable environments
Evaluate and optimize AI model hosting on AWS Bedrock — including model selection, performance benchmarking, and cost trade-offs across providers
Drive adoption and standardization of AI coding agent tooling (Claude Code, Cursor) across engineering teams, and evaluate emerging tools as the ecosystem evolves
Partner with Security to deploy and operate SAST, DAST, and SIEM solutions as part of our Secure SDLC