Senior Applied AI Engineer (Computer Vision)

Auxo AI; Gurugram District; Hybrid; INR 3,800,000 - 6,500,000; Full time

Job summary

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

You will build end-to-end visual intelligence systems, combining deep learning, classical CV techniques, and multimodal models, with ownership of deployment and real-world performance. Location options Mumbai, Bangalore, Hyderabad, Gurgaon; Hybrid – 3 days in office.

Qualifications

• 5+ years of experience building computer vision systems in production environments.
• Hands-on with detection, segmentation, or tracking systems.
• Experience training, fine-tuning, and evaluating models.
• Strong understanding of representation learning and loss functions (contrastive, focal).
• Experience deploying end-to-end vision systems, not just training.

Responsibilities

• Design and deploy computer vision systems for object detection, segmentation, tracking.
• Develop multimodal systems combining vision and language.
• Optimize for low-latency, real-time inference, scalability.
• Integrate vision systems into multimodal AI pipelines and agent-based workflows.
• Apply geometric methods like calibration, epipolar geometry, and pose estimation when relevant.

Skills

Computer Vision

OpenCV

PyTorch

TensorFlow

End-to-End Vision Systems

Multimodal Systems

Tools

YOLO

DETR

Mask R-CNN

ViT

Swin Transformer

Job description

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)

Responsibilities

• 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

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

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

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

Skills:- Computer Vision and OpenCV