AI Engineering Manager

AI Engineering Manager

Full-Time 100000 - 125000 £ / year (est.) No working from home possible
Gravitas Recruitment Group Ltd

At a Glance

  • Tasks: Lead a new AI team and deliver innovative AI-powered features.
  • Company: Dynamic software development company focused on AI solutions.
  • Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
  • Other info: Join a fast-paced team with a focus on innovation and customer success.
  • Why this job: Shape the future of AI in a collaborative environment and make a real impact.
  • Qualifications: Experience in leading engineering teams and delivering AI systems.

The predicted salary is between 100000 - 125000 £ per year.

We are seeking an AI Engineering Manager to build and lead a new AI team within a product-led software development environment. You will shape strategy and execution for AI-powered features and internal AI capabilities, ensuring measurable customer outcomes, high-quality delivery, and scalable engineering practices.

Location: 2 days a week (Hybrid), London, United Kingdom

Salary: £100,000 - £125,000 per year

Key Responsibilities:

  • Build and lead a new AI team, setting direction, standards, and ways of working.
  • Own end-to-end delivery of production AI systems, from discovery to launch, monitoring, and iteration.
  • Partner closely with customers and internal stakeholders to translate business needs into technical deliverables.
  • Drive agentic workflows and AI tooling adoption across the product development lifecycle to deliver tangible value.
  • Establish robust evaluation, observability, and quality practices for AI systems, balancing speed with reliability.
  • Guide teams through ambiguity and rapid change, making pragmatic decisions and removing blockers.
  • Measure success by customer outcomes, not just features shipped.

Experience Required:

  • Experience leading engineering teams that build and scale end-to-end production systems.
  • People leadership rooted in a software engineering background, with strong experience implementing AI internally within small to medium-sized product-led organisations.
  • Deep understanding of current AI tooling and the agentic workflow landscape, and how these approaches create value in product development.
  • Hands-on experience with AI models, tools, and frameworks, including agent orchestration, prompt engineering, RAG pipelines, evaluation frameworks, LangChain, Codex, Claude, Gemini, and observability tools and best practices.
  • Strong technical problem-solving skills and the ability to guide teams through ambiguous, fast-changing environments.
  • Excellent communication skills across technical and non-technical audiences.
  • A leadership style that blends high standards with empathy, fostering both speed and quality.
  • Proven ability to work closely with customers and translate needs into high-impact deliverables.

What You’ll Bring:

  • A track record of delivering reliable AI-enabled products or platforms in production.
  • Sound engineering judgement around security, privacy, and responsible AI practices.
  • Coaching and mentoring capability to develop engineers and create a strong team culture.

AI Engineering Manager employer: Gravitas Recruitment Group Ltd

Join a forward-thinking company that prioritises innovation and collaboration in the heart of London. As an AI Engineering Manager, you will not only lead a dynamic team but also benefit from a hybrid work model that promotes work-life balance. With a strong emphasis on employee growth, you'll have access to continuous learning opportunities and a culture that values both high standards and empathy, making it an ideal environment for those seeking meaningful and rewarding employment.

Gravitas Recruitment Group Ltd

Contact Details:

Gravitas Recruitment Group Ltd Recruitment Team

We think you need these skills to ace AI Engineering Manager

AI Team Leadership
End-to-End AI System Delivery
Customer Engagement
AI Tooling Knowledge
Agentic Workflow Implementation
Evaluation and Observability Practices
Technical Problem-Solving