AuxoAI is hiring a Senior Applied AI Engineer to design and deploy production-grade computer vision systems that operate reliably in real-world environments.

This role focuses on building end-to-end visual intelligence systems combining deep learning classical computer vision techniques and multimodal models. It is not limited to model training and requires strong ownership of system design deployment and real-world performance.

You will work on systems that perform perception understanding and reasoning over visual data and integrate these capabilities into larger AI platforms and agent-based workflows.

You will also work on problems where existing approaches may not be sufficient and will be expected to combine deep learning geometric methods and multimodal reasoning to build robust production-grade systems.

Location Mumbai / Bangalore / Hyderabad / Gurgaon (Hybrid 3 days per week in office)

Responsibilitiesp:

• Design and deploy computer vision systems for tasks such as:
  
  • Object detection segmentation and tracking
    
  • Scene understanding and structured perception
    
  • Video understanding and temporal reasoning
    
• Build and optimize models using architectures such as:
  
  • CNNs (ResNet EfficientNet)
    
  • Vision Transformers (ViT Swin DeiT)
    
  • Detection/segmentation models (YOLO DETR Mask R-CNN)
    
• Develop multimodal systems combining vision and language:
  
  • CLIP-style models
    
  • Vision-language models (VLMs)
    
  • Visual grounding and captioning systems
    
• Implement algorithms for:
  
  • Multi-object tracking (SORT DeepSORT ByteTrack)
    
  • Feature matching and representation learning
    
  • Temporal modeling (RNNs Transformers for video)
    
• Apply geometric and classical computer vision methods where relevant:
  
  • Camera calibration
    
  • Epipolar geometry
    
  • Pose estimation
    
  • 3D reconstruction or depth estimation
    
• Optimize systems for:
  
  • Low-latency real-time inference
    
  • Throughput and scalability
    
  • Edge and distributed deployment
    
• Design and build data pipelines for:
  
  • Annotation workflows
    
  • Dataset curation
    
  • Synthetic data generation
    
• Integrate vision systems into:
  
  • Multimodal AI pipelines
    
  • Agent-based systems
    
  • Decision-making workflows
    

Requirements

• 5 years of experience building computer vision systems in production environments
  
• Strong experience with deep learning frameworks (PyTorch / TensorFlow)
  
• Hands-on experience with:
  • Detection segmentation or tracking systems
    
  • Model training fine-tuning and evaluation
    
• Strong understanding of:
  • Representation learning
    
  • Loss functions (contrastive loss focal loss etc.)
    
  • Evaluation metrics (mAP IoU precision/recall)
    
• Experience building and deploying end-to-end vision systems not just training models
  

Candidates whose primary experience is limited to academic projects or model experimentation without real-world deployment may not be a fit for this role.

Nice to Have:

• Experience with multimodal systems (vision language)
  
• Familiarity with models such as:
  • CLIP BLIP Flamingo or similar
    
• Experience with 3D vision:
  • NeRFs
    
  • SLAM
    
  • Point clouds
    
• Experience with video understanding:
  • Action recognition
    
  • Event detection
    
• Experience building data engines:
  • Active learning
    
  • Hard negative mining
    
• Experience working with large-scale datasets and distributed training pipelines