AI Model Developer - Computer Vision

Mantras2success Consultants; Rajkot; On-site; INR 900,000 - 1,300,000; Full time

Benefits offered by this job

Health insurance

Job summary

Mantras2success Consultants is seeking an AI Model Developer specializing in computer vision to build generalized models for camera imagery, from data annotation planning to deployment.

You will lead model development, hyperparameter tuning, annotation strategy, and end-to-end ML pipelines, with a strong emphasis on real-time performance and robust evaluation across diverse environments.

Qualifications

• Bachelor's or Master's in CS/AI/related field.
• Experience with PyTorch or TensorFlow for model development.
• Hands-on CV tasks: detection, segmentation, or classification.
• Experience building ML pipelines and MLOps workflows.
• Familiarity with annotation tools and quality management.

Responsibilities

• Model Development: Develop and optimize deep learning models for camera images across domains.
• Hyperparameter Tuning: Systematically experiment with architectures and params for accuracy/speed balance.
• Annotation Management: Plan, review, and design labeling strategies to improve model understanding.
• Model Training & Evaluation: Train on curated datasets and compare to baselines.
• Automation & MLOps: Build data/model pipelines, track experiments and automate training/evaluation loops.
• Experimentation & Research: Explore new architectures and multimodal approaches for surveillance use cases.

Skills

Model Development

Anotation Management

Automation

Mlops

Model Training

Education

Bachelor's or Master's in Computer Science or AI/ML

Tools

PyTorch

TensorFlow

Roboflow

CVAT

MLflow

DVC

Kubeflow

Job description

AI Model Developer - Computer Vision Job Description:

This role blends hands‑on data labelling, model experimentation, and performance tuning to ensure the platform delivers robust and real‑time vision analytics.

We are looking for an AI Model Developer who will build generalized computer vision models for camera imagery. You will play a key role in developing, fine‑tuning, and optimizing models that can generalize across diverse camera feeds and environments. The role requires strong ownership of the full AI lifecycle - from data annotation planning to model deployment - and a passion for experimenting with different strategies to push model performance boundaries.

Key Responsiblities

• Model Development: Develop and optimize deep learning models for camera images, focusing on generalization across varied domains.
• Hyperparameter Tuning: Conduct systematic experiments with model architectures and hyperparameters to achieve the best trade‑off between accuracy, robustness, and speed.
• Annotation Management:
• Plan weekly annotation activities and strategies for the annotation team.
• Review and approve annotations to ensure quality and consistency.
• Design new labeling strategies to improve model understanding of complex scenarios.
• Model Training & Evaluation:
• Train and evaluate models on curated datasets.
• Compare new model performance against previous baselines to identify improvement trends.
• Analyze model failure cases and design corrective actions.
• Automation & MLOps:
• Develop and maintain data/model pipelines to automate repetitive or manual workflows.
• Implement experiment tracking, versioning, and automated training/evaluation loops.
• Experimentation & Research:
• Explore new architectures, augmentation techniques, and annotation strategies.
• Stay updated on recent trends in multimodal AI and apply them to complex surveillance use cases.

Requirements :

• Bachelor's or Master's degree in Computer Science, AI/ML, or related field.
• Strong experience with PyTorch or TensorFlow for model development.
• Hands‑on experience in computer vision tasks such as detection, segmentation, or classification.
• Proficiency in hyperparameter tuning and training large‑scale models.
• Familiarity with data annotation tools like Roboflow or CVAT and managing annotation quality.
• Experience in building ML pipelines and MLOps workflows (e.g., MLflow, DVC, Kubeflow).
• Analytical mindset for model evaluation and performance benchmarking.
• Excellent teamwork and communication skills.

Good-to-Haves

• Experience with multi-camera or multimodal data (e.g., image + metadata).
• Familiarity with Edge AI or real‑time video inference.
• Knowledge of data versioning, model drift detection, or continuous learning.
• Prior experience managing or coordinating a small annotation team.

Key Skills :

• Model Development
• Anotation Management
• Automation
• Mlops
• Model Training