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Nexxa.ai is building AI systems for heavy industries, enabling autonomous decision-making and action across manufacturing, infrastructure, and logistics. We're seeking an AI Vision Engineer to design, build, and deploy next-generation computer vision systems for real-world industrial applications.
You will work across the full vision stack—classical and deep-learning-based computer vision, vision-language models (VLMs), multimodal reasoning, and real-time inference on edge and cloud platforms. Responsibilities include:
- Design, train, evaluate, and deploy computer vision models for industrial applications
- Build and optimize CV pipelines for object detection, segmentation, classification, OCR, tracking, and visual understanding
- Develop and fine-tune VLMs for multimodal reasoning, visual question answering, and document understanding
- Design and optimize real-time inference pipelines for edge devices and cloud deployment
- Build scalable data pipelines for image/video collection, annotation, augmentation, training, and evaluation
- Fine-tune and evaluate open-source vision and multimodal foundation models
- Develop robust evaluation frameworks and benchmarks for accuracy, robustness, latency, and business impact
- Optimize models for production constraints (quantization, pruning, hardware-accelerated inference)
- Collaborate with product, engineering, and research teams to translate business requirements into technical solutions
- Contribute to architecture decisions, technical design reviews, and AI/CV best practices
- Stay current with advancements in computer vision, multimodal AI, and autonomous systems
You'll be a builder comfortable taking vision systems from prototype to production, working across classical CV, deep learning, and multimodal generative AI. The ideal candidate is curious, adaptable, collaborative, and thrives with autonomy while solving challenging real-world problems.
REQUIREMENTS:
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, Statistics, Artificial Intelligence, or related technical field (or equivalent practical experience)
- 3+ years of industry experience in Computer Vision, Machine Learning, Applied AI, or related fields
- Demonstrated experience independently owning and delivering computer vision projects from concept to production
- Strong Python programming skills
- Hands-on experience with PyTorch and modern deep learning workflows
- Experience developing and deploying computer vision models (detection, segmentation, classification, OCR) in production
- Experience with image/video processing libraries (e.g., OpenCV)
- Experience with CV/detection frameworks (YOLO, Detectron2, MMDetection, or similar)
- Experience working with VLMs, multimodal models, or Generative AI applications
- Strong understanding of ML fundamentals, model evaluation, experimentation, and deployment
- Experience with Hugging Face Transformers and open-source AI ecosystems
- Familiarity with data annotation workflows, dataset curation, hyperparameter optimization, and inference optimization
- Experience building production-grade software and AI systems
- Strong analytical, problem-solving, communication, and collaboration skills
PREFERRED QUALIFICATIONS:
- Master's degree in Computer Science, AI, Machine Learning, Computer Vision, or related field
- Experience with OCR, document understanding, or visual reasoning systems
- Familiarity with 3D vision, SLAM, or sensor fusion (camera + LiDAR) for industrial/robotics applications
- Experience with real-time inference optimization (TensorRT, ONNX Runtime, quantization, pruning)
- Experience deploying models on edge hardware (NVIDIA Jetson, embedded systems)
- Experience with LangChain, LangGraph, or agentic AI frameworks combining vision and language
- Experience with vector databases and visual/semantic retrieval systems
- Experience deploying AI systems on AWS, GCP, or other cloud platforms
- Experience with Docker, Kubernetes, and MLOps workflows
- Experience with PostgreSQL and large-scale data systems
- Experience with model serving, distributed training, and inference optimization at scale
- Contributions to open-source projects, technical blogs, research publications, Kaggle competitions, or demonstrable CV/AI work
- Experience in startup or high-growth environments
