Agent Specialist Lead - Vice President - GenAI Enablement
On-site · London, England, United Kingdom
Job Summary
Lead the deployment and support of LLM-powered agentic systems within the GenAI Enablement Team of the Chief Analytics Office, driving scalable, observable, high-performance production deployments. Collaborate with business stakeholders, product managers, data scientists, and engineering partners to translate requirements into agentic architectures tied to existing data and workflow platforms, and to operationalize LLM-powered agents that automate reasoning, retrieval, and workflow execution. Provide technical guidance to cross-functional teams, enhance agent orchestration, retrieval, and dynamic reasoning capabilities, and work with AI researchers and developers to advance agentic design, tool use, and multi-agent collaboration. Troubleshoot deployments with attention to risk controls and continuous performance improvement, ensuring robust, enterprise-grade solutions. Requires significant experience in AI/ML, hands-on multi-agent design (LangChain/LangGraph/AutoGen/CrewAI), Python proficiency, cloud and containerized deployments (Docker/Kubernetes), and a relevant Bachelor’s or Master’s degree.
Required Qualifications
- Significant experience in AI, machine learning, or intelligent systems, with recent experience building or deploying LLM-powered or agentic solutions
- Hands-on experience designing and implementing multi-agent systems using frameworks such as LangChain, LangGraph, AutoGen, or CrewAI, with practical understanding of LLM orchestration, retrieval augmentation (RAG), tool calling, and dynamic reasoning
- Experience integrating agentic systems into enterprise data and workflow environments, ensuring robustness, and maintainability
- Proficiency in Python, with experience extending orchestration components and building APIs or tool interfaces
- Experience deploying AI systems on cloud platforms using Docker, Kubernetes, and microservices integration
- Experience deploying and optimising GenAI and LLM-based systems, including performance evaluation and monitoring
- Strong analytical foundation with the ability to reason about system performance, model behaviour, and control trade-offs
- Proven ability to influence and align cross-functional teams through collaboration
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering or a related technical field required
- Strong communication skills for technical and non-technical audiences
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