Director / VP of Engineering (Computer Vision AI)

TLNT Bridge Recruiting•Toronto, ON

1d•CA$180,000 - CA$250,000•Hybrid

About The Position

Our client operates a high-scale physical operations platform used across large logistics yard environments in North America. The system processes millions of operational events monthly and integrates cameras, sensors, AI pipelines, and enterprise systems (TMS/WMS) into a single operational platform. The platform functions as mission-critical infrastructure for gate automation, yard operations, and real-time decisioning across distributed sites. This role owns the end-to-end engineering function and platform direction.

Requirements

10+ years in software engineering
5+ years in engineering leadership roles managing managers or senior engineers
Progression from developer to engineering management to senior leadership
Experience building and operating a recurring revenue product platform (B2B SaaS or equivalent)
Strong cloud architecture experience (AWS, GCP, or equivalent)
Experience with distributed systems and production-scale services
Demonstrated use of AI tools in a team-based engineering environment
Experience operating production systems where uptime and reliability are critical
Track record of scaling teams beyond hero-based execution models
Clear technical point of view on scaling complex distributed systems
Ability to identify risks across architecture, integrations, and teams
Practical experience improving engineering velocity using AI tools in real teams
Proven ability to remove dependency on key individuals through system design
Ability to balance speed with predictability in execution
Strong linkage between engineering decisions and business outcomes

Nice To Haves

GTA-based or willing to work primarily in Mississauga
Experience in both startup and large-scale engineering environments
Video streaming or real-time media systems (WebRTC, RTSP, HLS)
Computer vision systems in production environments
Hybrid cloud and on-prem infrastructure environments
Logistics, IoT, security, or industrial workflow systems

Responsibilities

Own technical direction across the full stack, including gate automation systems, site configuration and multi-tenant workflows, integrations with enterprise logistics systems (TMS/WMS), data architecture and event pipelines, operator-facing applications and console systems, and AI pipelines for video and sensor stream processing.
Make architectural decisions that are expected to scale over multiple years.
Build predictable delivery and planning systems.
Establish clear ownership across teams.
Improve risk management and delivery consistency.
Reduce reliance on hero-based execution.
Lead and develop engineering managers and senior engineers.
Strengthen organizational structure and accountability.
Hire and scale the next phase of the engineering team.
Maintain technical credibility within the leadership layer.
Embed AI tools into the development lifecycle as a core operating model.
Define standards for usage, review, and governance.
Measure impact on delivery speed, quality, and throughput.
Evolve workflows beyond experimental usage into systematic adoption.
Own uptime, observability, incident response, and operational readiness.
Ensure system resilience across distributed, real-world environments.
Establish reliability as a core engineering discipline.
Support rapid growth in customers, sites, and event volume.
Manage increasing system complexity and integration load.
Account for hybrid cloud and customer-hosted infrastructure environments.
Connect engineering execution to product adoption, implementation velocity, operational cost and margin, and customer retention and reliability outcomes.