AI Integration Lead

Bluecrestcapitalmanagement·Londonlead

In short

→Líder en integración de IA para equipos de oficina frontal, impulsando herramientas como Copilot y LLMs.
→Día a día: talleres, prototipos simples, capacitación y apoyo en procesos reales con IA.
→Destacado: liderazgo en adopción responsable y segura de IA, alineada con marcos de riesgo y control.

Fluent English required for collaboration and documentation.

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What they ask for

✓Experiencia en integración de IA en entornos empresariales.
✓Capacidad para comunicar conceptos técnicos a públicos no técnicos.
✓Conocimiento práctico de LLMs, RAG y agentes de IA.
✓Habilidades en Python y uso de APIs de IA aprobadas.
✓Experiencia en capacitación y creación de materiales de aprendizaje.
✓Capacidad para trabajar con equipos técnicos y no técnicos en procesos reales.

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PythonLLMsRAGAgent-assisted workflowsMicrosoft CopilotChatGPTClaudeMS Copilot StudioInternal librariesApproved AI APIs

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Department overview: The Front Office AI Technology Team sits within the Front Office Technology department and provides a shared capability for the development, operation, and adoption of AI across the firm. The team is responsible for building and supporting enterprise‑grade AI capabilities, including LLM‑powered applications, retrieval‑augmented generation (RAG) systems, agent‑assisted workflows, and scalable internal AI tooling. We develop the core AI foundations required to deploy AI safely and at scale, while also working closely with the business to ensure these capabilities are used effectively in day‑to‑day workflows. This includes supporting experimentation, guiding practical adoption, and helping teams embed AI into real processes where it delivers measurable benefit. A key focus of The Front Office AI Technology Team is ensuring that AI solutions are reliable, secure, and aligned with the firm’s control and risk frameworks. The team balances innovation with discipline, providing common tooling, patterns, and guidance that allow AI to be used consistently and responsibly across research and operational contexts. Role overview: Key Responsibilities: AI Enablement & Integration • Act as a go‑to point of contact for teams who want to understand what AI tools are available and how to use them effectively. • Run workshops, demos, and hands‑on sessions that help users understand the benefit of using LLMs and emerging AI related technologies both for business users and more technical teams. • Support day‑to‑day adoption of Microsoft Copilot, ChatGPT and Claude alongside other approved AI tools by embedding them into real workflows. Training & Best Practice • Deliver practical training on prompt engineering, AI limitations, and good usage patterns for both technical and non‑technical audiences. • Create and maintain reusable materials such as prompt examples/libraries, walkthroughs, and short guidance notes. • Continuously refine training content based on user feedback and emerging best practice. Understanding Business Processes • Spend time with teams to understand their existing processes, pain points, and where work is slow, repetitive, or manual. • Help teams articulate problems clearly enough that AI tooling can be applied sensibly. • Map simple end‑to‑end workflows and identify realistic opportunities for AI assistance. Light Prototyping & Applied AI • Build simple prototypes or proof‑of‑concept workflows using Python, internal libraries, or approved AI APIs alongside tools such as MS Copilot Studio. • Pair with engineers or platform teams when ideas move beyond quick prototypes. • Focus on small, shippable improvements rather than large, speculative solutions. Feedback & Continuous Improvement • Collect structured feedback on what works, what doesn’t, and where users get stuck. • Share insights with The Front Office AI Tech Team, governance, and Infrastructure Tech teams to help guide tooling, documentation, and prioritisation. • Help surface recurring themes rather than one‑off requests. Adoption Metrics, Reporting & Insight • Support the definition and tracking of adoption metrics for approved internal AI tools. • Work with Front Office Technology and platform teams to help maintain simple reporting and dashboards that show usage and engagement patterns (for example: active users, frequency of use, and common use cases). • Monitor adoption trends and identify areas of low engagement or friction that may require additional enablement, training, or tooling changes. • Combine quant

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