Senior Machine Learning Engineer – REMOTE UK - taking models from PoC into cloud systems is essential
Python, deploying models; Guide models from proof of concept to production; overseeing the full lifecycle of machine learning models; taking models into production in cloud systems; MLOps
Are you ready to harness the power of AI and data science to impact industries as diverse as satellite communications, energy management, and healthcare?
My client is looking for a Senior Machine Learning Engineer to take on a key role in transforming concepts into cutting-edge solutions, directly shaping the future of advanced technologies. This is an opportunity to lead with your expertise and see your innovations thrive across multiple high-stakes projects. Experience of taking models from PoC into cloud systems is essential.
Why You Should Apply

• Work at the intersection of AI, advanced mathematics, and real-world impact.
  
• Lead multiple projects with direct customer interaction and influence.
  
• Be a technical authority in a forward-thinking, collaborative environment.
  
• Mentorship and leadership opportunities, including developing the next generation of ML engineers.
  
• Flexible work arrangements with a mix of remote and office options.
  
• Enhanced leave benefits, mental health support, and a generous pension.
  What You’ll Be Doing
  
• Build, train, and monitor robust machine learning pipelines.
  
• Lead model architecture and MLOps development across multiple platforms.
  
• Collaborate with consultants, engineers, and customers on cutting-edge projects.
  
• Drive model trustworthiness through confidence quantification and explainability.
  
• Mentor and lead teams, fostering innovation and growth.
  About You
  
• Be able to do the job as described.
  
• Expertise in Python, MLOps, and cloud services.
  
• Experience taking models from PoC into cloud systems
  
• Comfortable managing multiple, complex projects simultaneously.
  
• A technical leader who can guide both clients and team members.
  
• Experience in confidence quantification and model explainability
  Please apply via the link for immediate consideration