Lead AI/ML Engineer β€” Scale Responsible AI Platforms in London

Lead AI/ML Engineer β€” Scale Responsible AI Platforms in London

London Full-Time 80000 - 100000 Β£ / year (est.) No working from home possible
Faculty

At a Glance

  • Tasks: Lead complex AI/ML projects and ensure scalable model performance.
  • Company: Join a forward-thinking faculty focused on responsible AI innovation.
  • Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
  • Other info: Dynamic role with significant impact on high-stakes AI platforms.
  • Why this job: Shape the future of AI while collaborating with talented teams.
  • Qualifications: Proven experience in AI/ML and strong leadership skills.

The predicted salary is between 80000 - 100000 Β£ per year.

Faculty is seeking a Lead AI/ML Engineer to set technical direction for complex AI/ML projects, ensuring scalable model performance. The ideal candidate will manage high-risk AI-powered platforms and define project roadmaps, guiding teams through multiple workstreams.

In this role, you'll leverage your expertise to justify architectural choices, drive developments in high-stakes environments, and align technical teams with business objectives. The position emphasizes collaboration and innovation within the AI sector.

Lead AI/ML Engineer β€” Scale Responsible AI Platforms in London employer: Faculty

At Faculty, we pride ourselves on being an exceptional employer that fosters a culture of collaboration and innovation in the AI sector. Our commitment to employee growth is evident through continuous learning opportunities and a supportive environment that encourages creative problem-solving. Located in a vibrant tech hub, we offer competitive benefits and the chance to work on high-impact projects that shape the future of responsible AI.

Faculty

Contact Details:

Faculty Recruitment Team

We think you need these skills to ace Lead AI/ML Engineer β€” Scale Responsible AI Platforms in London

AI/ML Expertise
Technical Direction Setting
Scalable Model Performance
Project Management
Architectural Justification
High-Risk Platform Management
Team Collaboration