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Technical Lead - AI/ML & Computer Vision

SR2 - Socially Responsible RecruitmentLondon🇬🇧United Kingdom

Quick overview

SeniorityMid Senior
Employment typeTemporary/Casual
Work modeHybrid
LocationLondon, United Kingdom
Stakeholder ManagementDockerMLOpsMLflowComputer VisionDeep LearningPyTorchPythonKubernetesMachine LearningCI/CD

Job description

Technical Lead - AI/ML & Computer Vision | Defence Technology

We are seeking an experienced AI/ML Technical Lead to lead the engineering delivery of an advanced computer vision and machine learning platform for a high-profile UK defence technology programme.

Working with a specialist AI organisation, you'll play a key role in taking established, cutting-edge AI technology into full-scale operational deployment.

This is a hands-on technical leadership position, combining advanced machine learning engineering, architecture and delivery oversight.

Key Responsibilities
  • Provide technical leadership to a team of AI/ML and MLOps Engineers.

  • Lead the design, optimisation and deployment of advanced computer vision and deep learning systems.

  • Oversee scalable ML training, inference and deployment pipelines.

  • Guide technical architecture, engineering standards and best practices.

  • Work closely with stakeholders to integrate AI solutions into existing infrastructure.

  • Mentor engineers, resolve technical challenges and ensure successful delivery.


Essential Skills & Experience
  • Active SC clearance - client can sponsor DV
  • Extensive experience in Machine Learning, Deep Learning and Computer Vision.

  • Strong hands-on Python and PyTorch development skills.

  • Expertise in CNNs, Vision Transformers and neural network architectures.

  • Proven experience delivering production-grade AI/ML systems.

  • Previous technical leadership or principal engineering experience.

  • Strong understanding of MLOps, CI/CD, model deployment and scalable infrastructure.

  • Excellent stakeholder management and technical communication skills.


Desirable Experience
  • GPU optimisation, distributed training and inference acceleration.

  • Docker, Kubernetes, MLflow or Kubeflow.

  • Object detection, segmentation and large-scale imagery processing.

  • Edge AI and NVIDIA technologies.

  • Defence, aerospace or national security projects.

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