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: Solid 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.

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📌 Machine Learning Engineer – Computer Vision (Ontario)
🏢 Infoya
📍 Ontario