Senior Software Engineer

HRB; Unorganized Thunder Bay District; Hybrid; CAD 85,000 - 115,000; Full time

Job summary

A technology solutions provider in Ontario is seeking a talented engineer to design and implement software infrastructure for deploying AI applications in manufacturing. The ideal candidate will have strong proficiency in Python, experience in building production-grade software systems, and knowledge of cloud computing and edge devices. This role involves integrating AI systems for quality control and operational optimization in an industrial environment.

Qualifications

• Strong proficiency in Python for production software development.
• Proven experience architecting infrastructure solutions that ensure uptime.
• 3-5 years of experience in building production-grade software systems.
• Experience with cloud computing platforms for hybrid edge-cloud deployments.

Responsibilities

• Design and implement robust software infrastructure for AI applications.
• Build and maintain production-grade software applications on edge devices.
• Implement monitoring and error recovery systems for AI systems.
• Develop software interfaces for AI vision systems.
• Contribute to building AI-powered platforms for facility operations.

Skills

Python proficiency

Infrastructure solutions

Production-grade software systems

Cloud computing experience

Linux systems

Containerization technologies (Docker)

Computer vision workflows

Application reliability principles

Debugging and problem-solving skills

Tools

NVIDIA Jetson

Job description

About Us

Our solution generates AI-powered actions and insights using off-the-shelf hardware or existing vision systems for real-impact manufacturing problems in products and equipment inspection, production efficiency, safety, and more.

Requirements

Role & Responsibilities

• On-Premise Infrastructure Architecture: Design and implement robust software infrastructure for deploying vision-based AI applications directly on manufacturing floor devices and edge computing platforms.
• Production Software Development: Build and maintain production-grade software applications on Linux-based edge devices, including AI inference pipelines, image processing workflows, and system monitoring solutions.
• Reliable Operations Management: Implement comprehensive monitoring, logging, alerting, and error recovery systems to ensure high availability and reliability of deployed AI systems in industrial manufacturing environments.
• Vision System Integration: Develop software interfaces for AI vision systems addressing manufacturing quality control, productivity optimization, safety monitoring, and equipment uptime challenges.
• Data Platform Development: Contribute to building AI-powered platforms that provide data analysis for connected facility operations, including data collection, processing, and analytics pipelines.
• IoT & Fleet Management: Build and support device management systems for on-premise AI deployments, including remote monitoring, configuration management, and fleet-wide software orchestration across manufacturing sites.
• OTA Deployment Systems: Design and implement over-the-air software update mechanisms for distributed on-premise devices, ensuring safe and reliable remote updates with minimal production disruption.
• Industrial Integration: Collaborate with hardware teams to integrate AI applications with PLCs, existing industrial automation infrastructure, and manufacturing execution systems.
• Performance Optimization: Profile and optimize software performance for resource-constrained edge environments and real-time processing requirements in manufacturing settings.

Must-Have

• Strong proficiency inPython for production software development and system architecture
• Proven experience architecting and building successfulinfrastructure solutions that ensure uptime and reliability of real-time on-premise applications
• 3–5 years of experience in building production-grade software systems, preferably for industrial or manufacturing environments
• Cloud computing experience with major platforms (AWS, Azure, GCP) for hybrid edge-cloud deployments and infrastructure management
• Hands-on experience withLinux systems, command line operations, and system administration for edge computing platforms
• Experience withcontainerization technologies (Docker) and deployment of applications in production environments
• Understanding ofcomputer vision workflows and AI inference pipelines for manufacturing applications
• Knowledge ofapplication reliability principles: monitoring, alerting, graceful degradation, error recovery, and system health management
• Understanding ofmanufacturing environments and challenges related to quality control, productivity, safety, and equipment uptime
• Strong debugging and problem-solving skills inproduction environments with minimal downtime tolerance

Strongly Preferred

• Full-stack web development experience withTypeScript and React for building operator interfaces and dashboards
• Experience withIoT protocols and device management for industrial environments (MQTT, HTTP/REST APIs, industrial networking)
• Experience withover-the-air (OTA) software deployment and update mechanisms for on-premise industrial devices
• Experience withNVIDIA Jetson or similar edge computing platforms for AI deployment in manufacturing
• Knowledge ofindustrial automation protocols (Modbus, Ethernet/IP, OPC-UA) andPLC integration

Nice To Have

• Experience withtime-series databases and analytics platforms for manufacturing data (InfluxDB, Grafana, Prometheus)
• Background incomputer vision libraries (OpenCV) and machine learning frameworks (TensorFlow, PyTorch) deployment
• Familiarity withmanufacturing execution systems (MES) and quality management systems
• Experience withdevice management platforms for industrial IoT deployments
• Understanding ofcybersecurity best practices for on-premise industrial systems
• Knowledge ofdata pipeline architectures for connected facility analytics
• Experience infood & beverage, CPG, automotive, or packaging manufacturing environments

Preferred Candidate Profile

• On-Premise Deployment Experience: Candidates who have deployed and maintained software systems directly in industrial/manufacturing environments, addressing network constraints, security requirements, and uptime expectations
• Production Reliability Background: Experience in production systems where downtime has direct business impact (manufacturing, industrial automation, critical infrastructure)
• Vision/AI Application Deployment: Experience deploying computer vision or AI applications in real-world production environments, with an understanding of model performance, data quality, and system integration challenges
• Manufacturing Domain Knowledge: Understanding of manufacturing processes, quality control requirements, and operational constraints in production environments
• Infrastructure Mindset: Candidates who prioritize system architecture, scalability, monitoring, and long-term maintenance—not just feature development
• Edge Computing Experience: Familiarity with resource-constrained environments, edge device management, and distributed system challenges in industrial settings