AI/ML Manager
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About the Role
We have partnered with a large, diversified conglomerate to hire an AI/ML Manager. This is a hands-on technical leadership role at the heart of the Group's AI delivery programme.
Key Skills for This Role
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Overview
We have partnered with a large, diversified conglomerate to hire an AI/ML Manager.
This is a hands-on technical leadership role at the heart of the Group's AI delivery programme.
The business is building and deploying real AI systems across multiple functions, and this person will own the engineering of those systems from design through to production.
If you want to work on AI that actually ships and actually matters, this is the role.
This role suits someone with deep applied AI experience who is ready to lead delivery across a cross-functional engineering team without losing their technical edge.
About The Role
- Design, build and deploy production-grade AI and ML systems including LLM-based applications, RAG pipelines and GenAI workflows across the Group
- Translate business use cases into scalable, well-architected technical solutions that solve real problems rather than demonstrate capability
- Develop APIs, integrations and orchestration workflows that connect AI systems into broader platform and product architecture
- Lead delivery across small engineering pods, maintaining quality, pace and clear accountability throughout
- Ensure all systems meet production readiness standards including performance, scalability, monitoring and fault tolerance
- Stay current with the rapidly evolving AI landscape and bring emerging techniques and tooling into the team where they add genuine value
- About you:
- Strong Python skills and deep applied AI/ML experience, with a track record of building and deploying models into production environments
- Hands-on experience with LLM APIs, RAG system design and vector databases such as Pinecone, Weaviate or pgvector
- Proficiency with orchestration frameworks such as LangChain, LlamaIndex or equivalent
- Solid understanding of system design, APIs and how AI components integrate into broader engineering architectures
- Comfortable leading engineering teams and managing delivery across multiple workstreams simultaneously
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