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Sr. Computer Vision & AI Engineer

PixoAnalytics
London
1 month ago
Applications closed

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Key Responsibilities

  • Design & Implement Computer Vision Solutions: Develop and optimize end-to-end computer vision pipelines using advanced deep learning techniques.
  • LLMs-VLMs: Have awareness about the latest technologies in the field of LLMs and VLMs.  
  • Model Development & Deployment: Build, train, and deploy deep learning models (e.g., object detection, image segmentation, classification) using frameworks like PyTorch and TensorFlow.
  • Data Pipeline & Infrastructure: Work closely with data engineers to ensure efficient data preprocessing, augmentation, and real-time inference pipelines.
  • Docker & REST APIs: Containerize applications for scalable deployments and create robust RESTful APIs for seamless integration with other services.
  • UI Development (PyQt): Develop or integrate user interfaces for internal tools or customer-facing applications.
  • Model Monitoring & Experiment Tracking: Leverage tools like Weights & Biases (wandb) or MLflow to track experiments, monitor model performance, and ensure continuous improvement.
  • Performance Optimization: Conduct performance tuning and hardware optimization (GPU/CPU) to achieve high throughput and low latency.
  • Collaboration & Mentorship: Work in cross-functional teams (Product, Data, DevOps) and mentor junior developers on best practices and new technologies.


Required Qualifications

  • 5+ years of hands-on experience in Computer Vision and Deep Learning.
  • Fluency in Python; additional programming languages (C++, Java, etc.) are a plus.
  • Expertise in Deep Learning Frameworks: PyTorch and TensorFlow.
  • Proficiency with Docker for containerization and microservices.
  • Experience with RESTful API design and implementation.
  • Knowledge of PyQt (or similar frameworks) for desktop UI development.
  • Familiarity with Model Monitoring & Experiment Tracking (Weights & Biases, MLflow, etc.).
  • Strong background in linear algebra, calculus, and probability/statistics as they relate to ML.
  • Excellent problem-solving and debugging skills.
  • Bachelor’s/Master’s/PhD in Computer Science, Electrical Engineering, or a related field (or equivalent work experience).


Preferred Skills & Nice-to-Haves

  • Experience with DevOps practices (CI/CD, Kubernetes).
  • Familiarity with Cloud Platforms (AWS, Azure, GCP) for model deployment and scaling.
  • Understanding of Edge Computing and on-device model optimization (TensorRT, ONNX).
  • Knowledge of NVIDIA CUDA for GPU acceleration.
  • WANDB and MLflow for training monitoring.
  • Contributions to open-source computer vision or deep learning projects. 


What We Offer

  • Competitive Compensation
  • Flexible Work Arrangements (Remote) and a positive work-life balance.
  • Growth Opportunities: A chance to lead cutting-edge projects and mentor junior developers.
  • Collaborative Culture: Work alongside passionate professionals in an environment that values innovation and continuous learning.



National AI Awards 2025

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