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Maximl Labs
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Computer Vision Engineer

Computer Vision Engineer

Maximl Labs

1 - 2 years

3 - 7 Lacs

Bengaluru

Posted:17 hours ago| Platform:

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Skills Required

QA Computer vision deep learning GIT Debugging Deployment model development Open source SQL Python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Model Development

Implement and fine-tune moderndetection/classification architectures like YOLOv8, Faster/Mask RCNN,EfficientNet, ConvNeXt, and Vision Transformers.

Conduct ablation studies,hyperparameter tuning, and model optimization experiments (e.g., pruning,quantization, distillation).

Data Pipeline

Define labeling guidelines,manage annotation QA loops, and handle class imbalance strategies likere-sampling or focal loss.

Build data loaders andaugmentation pipelines using libraries such as Albumentations or TorchVision,tailored to challenging industrial imagery.

Evaluation & QA

Design reproducible experimentswith clear metric dashboards (mAP, F1 score, PR curves).

Perform error analysis andmodel debugging to uncover edge-case failure modes.

Deployment

Package models for deploymenton cloud services (e.g., Azure, AWS).

Integrate models intoproduction workflows using REST APIs, Docker, and CI/CD pipelines.

Collaboration

Work closely withcross-functional teams to translate real-world use cases into model specs.

Document code and experimentsthoroughly and contribute to weekly research reviews.

Requirements

Key Requirements for the Role :

13 years hands-on experience in computer vision ordeep learning roles

Experience with industrial/safetyinspection datasets (e.g., PPE detection, visual defect classification).

Familiarity with MLOps tools likeMLflow, DVC, or ClearML.

Experience with model optimizationand deployment frameworks (ONNX, TensorRT, OpenVINO).

Exposure to real-time or edgeinference performance constraints.

Contributions to open-source,research publications, or competitive CV challenges (e.g., Kaggle).

Technical Qualification:

Proficiency in Python and deep learning frameworks (PyTorch /Tensorflow).
Good understanding of CNNs, transfer learning, data augmentation, and overfitting mitigation.
Familiarity with basic software engineering practices (git, code reviews, unit testing).
Mathematical Background: Solid grasp of linear algebra, probability, and optimization as applied in ML .
Languages/Frameworks: Python, PyTorch, Tensorflow, TorchVision, FastAPI, OpenCV
Model Tools: ONNX, TensorRT, Albumentations
DevOps : Docker, Git, Azure/AWS
Infra: Jetson devices, cloud APIs, SQL/NoSQL databases

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Computer Vision Engineer