Machine Learning Engineer – Computer Vision

Infoya; Toronto; Hybrid; CAD 100,000 - 110,000; Full time

Job summary

Infoya in Toronto is seeking a Machine Learning Engineer – Computer Vision to design, train, and deploy deep learning models for real-time edge inference across devices. You will work on end-to-end CV model lifecycles—from training to automated governance and edge deployment—while advancing MLOps capabilities on Google Cloud.

You will join a team applying CNNs and modern DL frameworks, optimizing models for edge devices with TF Lite, and integrating data pipelines and cloud infrastructure.

Qualifications

• 3–6 years of ML engineering experience, preferably in Computer Vision.
• Strong mathematical foundation and understanding of CNNs and object detection architectures.
• Hands-on experience with TensorFlow 2.x and/or PyTorch.
• Proven edge ML experience using TF Lite, quantization, pruning, and custom ops.
• GCP MLOps experience building and running Vertex AI Pipelines (KFP).
• Proficient in Python and containerizing ML workloads with Docker.
• Understanding of GCS for large datasets and model handoffs.
• Excellent communication and problem-solving abilities.

Responsibilities

• Design, train, evaluate and fine-tune CNN-based models for image classification and object detection.
• Maintain and enhance MLOps capabilities with Vertex AI/Kubeflow Pipelines.
• Convert models to edge-ready TF Lite artifacts and optimize for hardware acceleration.
• Architect the edge deployment flow using GCS and manage model versioning for device retrieval.
• Implement automated evaluation gates to ensure new models outperform production before edge deployment.

Skills

CNNs

PyTorch

TensorFlow

Python

Docker

GCP

Edge AI

MLOps

TFLite

KFP

CV

Tools

TensorFlow 2.x

PyTorch

Vertex AI Pipelines (KFP)

Docker

Google Cloud Storage

YOLOv8

Google Cloud Composer

Apache Beam / Dataflow

Job description

Infoya is a global IT solutions provider specializing in transforming complex challenges into streamlined, AI-powered outcomes. Through proprietary technology accelerators and full-scale enterprise services, Infoya automates workflows, enhances operational efficiency, and drives digital transformation across industries. With a presence in Canada, the US, India, and Costa Rica, we blend technical depth with creative problem-solving to deliver measurable impact.

Job Description

About the Job: We areseeking a seasoned Machine Learning Engineer – Computer Vision todesign, optimise, and deploy deep learning models for large-scale, real-timeedge inference. In this role, you will work on the end-to-end lifecycle ofcomputer vision models—from training and evaluation to optimisation, automatedgovernance, and edge deployment—while advancing MLOps capabilities on GoogleCloud. You will work at the intersection of deep learning, cloudinfrastructure, and edge AI, building reliable, high-performance solutions thatscale across devices and continuously improve through automation and datadriven evaluation.

Office Location: Toronto

Employment Type: Permanent

Work Arrangement: Hybrid (2days in office per week)

Position Responsibilities:

• Computer Vision Development: Design, train,evaluate, and fine-tune state-of-the-art deep learning models for imageclassification and object detection tasks.
  
• Pipeline Enhancement: Maintain, optimize and addadvanced MLOps capabilities to existing Vertex AI Kubeflow Pipelines(KFP).
  
• Model Optimization & Conversion: Manage thecomplex conversion of models from frameworks like TensorFlow into highlyoptimized TensorFlow Lite (TFLite) artifacts for edge inference (e.g.,handling Int8 full integer quantization and hardware-specific acceleration).
  
• Edge Artifact Management: Architect the deploymentflow to save optimized edge models to Google Cloud Storage (GCS) andmanage model versioning for seamless edge-device retrieval, bypassingtraditional Vertex AI Endpoints.
  
• Automation & Reliability: Implement automatedevaluation gates to ensure newly trained models outperform existingproduction models before edge deployment.
  

Requirements

Required Qualifications:

• Experience: 3- 6 years in Machine LearningEngineering, preferably Computer Vision.
  
• Deep Learning Foundation: Strong mathematical andarchitectural understanding of deep learning concepts, specificallyConvolutional Neural Networks (CNNs) and standard object detectionarchitectures.
  
• Framework Mastery: Deep, hands-on expertise withTensorFlow 2.x and/or PyTorch.
  
• Edge ML: Proven experience optimizing deep learningmodels for edge devices using TFLite (e.g., post-training quantization,pruning, handling custom ops).
  
• GCP MLOps: Strong proficiency in Google CloudPlatform, specifically building and running custom components in Vertex AIPipelines (KFP).
  
• Programming: Advanced programming skills in Python,with experience containerizing ML workloads using Docker.
  
• Cloud Infrastructure: Solid understanding of GoogleCloud Storage (GCS) for managing massive datasets and handling modelartifact hand-offs.
  
• Critical thinking, Effective communication skills –verbal and written, Problem solving, and Dealing with complexity
  

Preferred Qualifications:

• YOLO Expertise: Hands-on experience with theUltralytics YOLOv8 ecosystem, specifically bridging PyTorch YOLO weightsto TensorFlow/TFLite edge deployments.
  
• Data Orchestration: Experience using Google CloudComposer (Apache Airflow) to schedule and trigger complex ML trainingpipelines based on data arrival or model drift.
  
• Scalable Data Processing: Familiarity with GoogleCloud Dataflow (Apache Beam) for large-scale, parallelized imagepreprocessing, augmentation, and dataset formatting (e.g., generatingTFRecords).
  
• CI/CD for ML: Experience with continuousintegration and continuous deployment practices specifically tailored formachine learning models.
  
• Generative AI: Knowledge or experience inGenerative AI architectures, with experience building Retrieval-AugmentedGeneration (RAG) pipelines and developing multi-agent systems.
  

Salary Range: CAD $100,000- $110,000/ year

The final compensation offeredwill depend on local market conditions and geographic location, as well asjob-related factors such as the candidate’s knowledge, skills, qualifications,relevant experience, and education/training. Compensation may also includeadditional components such as benefits, and/or other incentives, whereapplicable. In accordance with new employment standards requirements, we retaincopies of this job posting and applicant information for three (3) years afterthe posting is removed. We do not use AI technology; all applications are alsoreviewed by our recruitment team.

Infoya is an equal opportunityemployer committed to diversity and inclusion. We welcome applications from allqualified individuals, regardless of race, color, religion, sex, sexualorientation, gender identity, national origin, age, disability, protected veteranstatus, aboriginal status, or any other legally protected factors.