Machine Learning Engineer Computer Vision

 
<\/p>

Office Location: <\/b> Toronto
<\/p>

Employment Type: <\/b> Permanent
<\/p>

Role Type: <\/b> New position –
current requirement
<\/p>

Work Arrangement: <\/b> Hybrid (2
days in office per week)
<\/p>

<\/b>
<\/p>

Position Responsibilities: <\/b>
<\/p>

• Computer Vision Development: Design, train,
  evaluate, and fine -tune state -of -the -art deep learning models for image
  classification and object detection tasks.
  <\/li>
• Pipeline Enhancement: Maintain, optimize and add
  advanced MLOps capabilities to existing Vertex AI Kubeflow Pipelines
  (KFP).
  <\/li>
• Model Optimization & Conversion: Manage the
  complex conversion of models from frameworks like TensorFlow into highly
  optimized TensorFlow Lite (TFLite) artifacts for edge inference (e.g.,
  handling Int8 full integer quantization and hardware -specific acceleration).
  <\/li>
• Edge Artifact Management: Architect the deployment
  flow to save optimized edge models to Google Cloud Storage (GCS) and
  manage model versioning for seamless edge -device retrieval, bypassing
  traditional Vertex AI Endpoints.
  <\/li>
• Automation & Reliability: Implement automated
  evaluation gates to ensure newly trained models outperform existing
  production models before edge deployment.
  <\/li><\/ul>

  
  <\/div><\/span>
  

  Requirements <\/h3>

  Required Qualifications: <\/b>
  <\/p>

  • Experience: 3 - 6 years in Machine Learning
    Engineering, preferably Computer Vision.
    <\/li>
  • Deep Learning Foundation: Strong mathematical and
    architectural understanding of deep learning concepts, specifically
    Convolutional Neural Networks (CNNs) and standard object detection
    architectures.
    <\/li>
  • Framework Mastery: Deep, hands -on expertise with
    TensorFlow 2.x and/or PyTorch.
    <\/li>
  • Edge ML: Proven experience optimizing deep learning
    models for edge devices using TFLite (e.g., post -training quantization,
    pruning, handling custom ops).
    <\/li>
  • GCP MLOps: Strong proficiency in Google Cloud
    Platform, specifically building and running custom components in Vertex AI
    Pipelines (KFP).
    <\/li>
  • Programming: Advanced programming skills in Python,
    with experience containerizing ML workloads using Docker.
    <\/li>
  • Cloud Infrastructure: Solid understanding of Google
    Cloud Storage (GCS) for managing massive datasets and handling model
    artifact hand -offs.
    <\/li>
  • Critical thinking, Effective communication skills –
    verbal and written, Problem solving, and Dealing with complexity
    <\/li><\/ul>

    <\/b>
    <\/p>

    Preferred Qualifications: <\/b>
    <\/p>

    • YOLO Expertise: Hands -on experience with the
      Ultralytics YOLOv8 ecosystem, specifically bridging PyTorch YOLO weights
      to TensorFlow/TFLite edge deployments.
      <\/li>
    • Data Orchestration: Experience using Google Cloud
      Composer (Apache Airflow) to schedule and trigger complex ML training
      pipelines based on data arrival or model drift.
      <\/li>
    • Scalable Data Processing: Familiarity with Google
      Cloud Dataflow (Apache Beam) for large -scale, parallelized image
      preprocessing, augmentation, and dataset formatting (e.g., generating
      TFRecords).
      <\/li>
    • CI/CD for ML: Experience with continuous
      integration and continuous deployment practices specifically tailored for
      machine learning models.
      <\/li>
    • Generative AI: Knowledge or experience in
      Generative AI architectures, with experience building Retrieval -Augmented
      Generation (RAG) pipelines and developing multi -agent systems.
      <\/li><\/ul>

      
      <\/div><\/span>
      

      Benefits <\/h3>

      Salary Range: <\/b> CAD $100,000
      - $110,000/ year
      <\/p>

       
      <\/p>

      The final compensation offered
      will depend on local market conditions and geographic location, as well as
      job -related factors such as the candidate’s knowledge, skills, qualifications,
      relevant experience, and education/training. Compensation may also include
      additional components such as benefits, and/or other incentives, where
      applicable. In accordance with new employment standards requirements, we retain
      copies of this job posting and applicant information for three (3) years after
      the posting is removed. We do not use AI technology; all applications are also
      reviewed by our recruitment team.
      <\/p>

      Infoya is an equal opportunity
      employer committed to diversity and inclusion. We welcome applications from all
      qualified individuals, regardless of race, color, religion, sex, sexual
      orientation, gender identity, national origin, age, disability, protected veteran
      status, aboriginal status, or any other legally protected factors.
      <\/p>

      
      <\/div><\/span>