AI Solutions Engineer

at  Robert Walters

Robert Walters are delighted to be partnering with Seechange once again to help expand their growing AI solutions team.
This AI Solutions Engineer would be joining a vibrant startup in Manchester with exciting projects and prospects ahead within hazard detection and fraud. Some of the world’s largest brands depend on SeeChange AI solutions. They enable Enterprise clients to optimise their business and solve real world problems using our AI powered Computer Vision This business is at the forefront of edge-to-cloud AI solutions, changing the way insights are derived from video sensors.

THE IDEAL CANDIDATE WITH HAVE EXPERIENCE WITHIN:

• Solid understanding of Machine Learning and Deep Learning concepts, particularly in computer vision.
• Proficiency in Deep Learning frameworks such as PyTorch, TensorFlow, or OpenVino.
• Strong programming skills in Python, with experience in ML/CV environments (Numpy, OpenCV).
• Familiarity with Linux operating systems and Git processes.
• Excellent communication skills, both written and oral, with a positive and enthusiastic attitude.
• Customer-focused mindset with a desire to see solutions in production environments.
• Knowledge of GPU acceleration and optimization is a plus.
  The role is offering a salary of up to £55,000 per annum and is working in Manchester City Centre on a hybrid working schedule. The position also comes with a wealth of benefits including private health care and a 7% pension contribution.
  Please apply if you would like to discover more.
  Robert Walters Operations Limited is an employment business and employment agency and welcomes applications from all candidates
  Robert Walters are delighted to be partnering with Seechange once again to help expand their growing AI solutions team

• Collaborate with the product team to understand requirements for new use cases and develop suitable solutions.
• Implement Image Classification & Image Processing techniques on existing models to detect what the video data is showing.
• Work closely with the models’ team to specify required ML models and enhance existing ones.
• Develop additional logic layers to optimize model performance and accuracy.
• Conduct end-to-end testing to ensure successful solution deployment in real-life customer environments.