Hiring: 2 Senior R&D Contractors (Multimodal AI – Image + Text)

🚀 Hiring: 2 Senior R&D Contractors (Multimodal AI – Image + Text)

💼 Contract: 3–6 Months | T&M | Staff Augmentation
🌐 Remote | 🕓 Immediate Start

Our client is building a next-generation multimodal AI model combining biometric image data (e.g., tongue images) with text-based clinical descriptions and diagnoses.
We are in the R&D phase and require highly skilled senior engineers who can build clinically meaningful, interpretable, and robust models from the ground up.

1️⃣ Senior Computer Vision / Image Processing Engineer

Role Overview

Own the complete image pipeline—preprocessing, color correction, segmentation, detection, and dataset annotation workflow design.

Key Responsibilities

  • Implement advanced color correction (Gray World, Retinex, SVM/SVR-based).
  • Perform morphological operations (erosion, dilation, opening/closing, interpolation).
  • Expert-level segmentation using:
    • Classical: Active Contour/Snakes, Watershed, Graph Cuts, Region Growing
    • Deep learning: UNet, UNet++, DeepLab, Mask R-CNN
  • Customize and fine-tune YOLO-family detection models.
  • Work with large datasets; define annotation protocols, tools, and taxonomies.
  • Ensure image pipeline supports clinical interpretability & robustness.

Required Skills

  • Python, OpenCV, NumPy
  • PyTorch / TensorFlow
  • UNet / UNet++ / DeepLab / Mask R-CNN
  • Classical segmentation (watershed, snakes, graph cuts)
  • Color correction methods
  • Morphological operations
  • Experience with Labelbox / CVAT / Supervisely
  • Designing taxonomies for clinical datasets

2️⃣ Senior Machine Learning / Deep Learning Engineer

Role Overview

Lead development of fusion models combining image + text modalities with custom neural architectures and interpretability-first design.

Key Responsibilities

  • Architect & optimize complex neural networks: CNNs, GCNs, KAN, interpretable models.
  • Fine-tune pre-trained models for clinical datasets.
  • Perform feature selection (LASSO, Random Forest, SHAP).
  • Build cross-modal fusion models (image + text) using:
    • Custom loss functions (alignment, consistency loss)
    • Attention-based fusion mechanisms
  • Evaluate using advanced metrics:
    • Segmentation: IoU, Dice, Hausdorff
    • Detection: mAP, Precision–Recall
    • Classification: F1, ROC, multi-label metrics
  • Guide annotation strategy for multi-label, clinically meaningful datasets.
  • Ensure models are interpretable, reliable, and clinically relevant.

Required Skills

  • Text + Image fusion
  • CNNs, GCNs, KAN
  • Custom loss function design
  • Attention mechanisms
  • Feature selection frameworks
  • Multi-label classification expertise
  • Deep metric understanding (IoU, Dice, mAP, PR, ROC)

📩 Apply / Refer Immediately

📧 Email: riyaz.ltpl@gmail.com
📱 WhatsApp: +91 9595175635
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