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Generative AI popularity, metro location, and broad senior ML/AI requirements increase candidate competition.
Technical AI skills are transferable but regulated financial governance and domain knowledge increase sensitivity.
Many mandatory technical must-haves, regulated environment experience, and governance requirements imply high filtering.
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Design, develop, deploy, and optimise AI and machine learning solutions including AI-powered applications, APIs, and automation workflows, focusing on generative AI and RAG/vector search architectures.
Ensure high code and model quality, system performance, observability, security, compliance, and responsible AI controls in a regulated financial services environment.
Collaborate cross-functionally with product, architecture, data, risk, and business teams to deliver impactful AI capabilities that improve customer outcomes, operational efficiency, and decision-making.
Strong software engineering skills in Python (or similar languages) with production-grade AI solution experience.
Hands-on expertise in generative AI, including LLMs, prompt engineering, embeddings, vector search, and Retrieval-Augmented Generation (RAG) pipelines.
Experience working in regulated environments (e.g., banking, insurance, pensions, or asset management) with governance, security, and operational resilience requirements.
Work Experience Required: Proven experience delivering quality AI and software solutions in enterprise-scale environments; specific years not explicitly mentioned in the JD.
Experienced in cloud platforms (preferably Microsoft Azure), containerisation (Docker, Kubernetes), CI/CD, and DevSecOps engineering within agile methodologies.
Subject matter expert on generative AI technologies and frameworks, with a strong focus on responsible AI governance, security, auditability, and infrastructure-as-code.
Capable of technical leadership and mentoring, collaborating across disciplines to translate business problems into scalable AI solutions in a regulated environment.