数据与人工智能主管 - 英国总部
Head of Data & AI - UK Based
关于StarCompliance
StarCompliance是员工合规技术领域的领先提供商，被350多家全球金融机构和高度监管的组织所信赖，用于管理监管义务、监控员工活动并降低风险。在私募股权支持下，我们正在投资下一代基于云的、具备人工智能功能的SaaS平台——而这个职位将塑造该平台的AI和数据未来。
职位描述：
我们需要一位亲力亲为的技术领导者来负责我们的数据和AI战略并加以执行——构建一个高性能的数据与AI团队，在高度监管的环境中大规模交付生产级别的AI能力。
你将与产品、工程、架构、安全以及高管团队紧密合作。你将与产品总监、AI与数据产品团队形成联合技术和产品领导配对，共同负责AI路线图、优先级和交付成果。
职责：
AI产品交付与战略
- 负责StarCompliance平台中AI驱动产品功能的端到端技术战略和执行路线图。
- 推动生成式AI、基于大语言模型（LLM）的架构、预测分析、图智能、推荐系统和语义搜索的应用，这些技术需能为客户带来可衡量的价值。
数据平台与工程
- 负责数据基础架构的战略，确保干净、可信、受良好治理的数据支撑每一个AI项目。
- 构建强大的MLOps能力，包括模型训练流水线、版本控制、监控、A/B测试和漂移检测。
内部AI赋能
- 在工程和产品开发中推动务实的AI应用，加速我们构建的方式，而不仅仅是构建的内容。
领导力与团队建设
- 建立、指导并扩展一个高性能的数据与AI组织，涵盖数据工程、数据科学、机器学习工程和分析。
跨职能与高管协作
- 与CTO和高管团队密切合作，确保AI和数据举措与公司战略和商业优先级保持一致。
- 与产品总监、AI与数据产品团队合作，共同负责AI路线图、优先级和交付成果。
技能与经验：
AI与机器学习
- 具有在大规模生产环境中成功交付AI和ML系统的经验，而不仅仅是实验。
- 在生成式AI和基于大语言模型（LLM）的应用架构方面有深入经验（微调、提示工程、RAG、代理系统等）。
查看英文原文
About StarCompliance
StarCompliance is a market-leading provider of employee compliance technology, trusted by 350+ global financial institutions and highly regulated organisations to manage regulatory obligations, monitor employee activity, and mitigate risk. Backed by private equity, we are investing in the next generation of our cloud-native, AI-enabled SaaS platform — and this role will shape the AI and data future of that platform.
Role:
We need a hands-on technical leader to own our data and AI strategy and execute it — building a high-performing Data & AI function that ships production-grade AI capabilities at scale in a heavily regulated environment.
You will partner closely with Product, Engineering, Architecture, Security, and the executive team. You will work together with the Product Director, AI & Data Products as a joint technology-and-product leadership pairing, co-owning the AI roadmap, prioritisation, and delivery outcomes.
Responsibilities:
AI Product Delivery & Strategy
- Own the end-to-end technical strategy and execution roadmap for AI-enabled product capabilities across the StarCompliance platform.
- Drive adoption of generative AI, LLM-based architectures, predictive analytics, graph intelligence, recommendation systems, and semantic search where these deliver measurable customer value.
Data Platform & Engineering
- Own the data foundation strategy, ensuring clean, trusted, well-governed data underpins every AI initiative.
- Build robust MLOps capabilities, including model training pipelines, versioning, monitoring, A/B experimentation, and drift detection.
Internal AI Enablement
- Champion pragmatic AI adoption within engineering and product development, accelerating how we build, not just what we build.
Leadership & Team Building
- Build, mentor, and scale a high-performing Data & AI organisation across data engineering, data science, ML engineering and analytics.
Cross-Functional & Executive Collaboration
- Work closely with the CTO and executive team to align AI and data initiatives with company strategy and commercial priorities.
- Partner with the Product Director, AI & Data Products, co-owning the AI roadmap, prioritisation, and delivery outcomes.
Skills and Experience:
AI & Machine Learning
- Proven delivery of production-grade AI and ML systems at scale, not just experimentation.
- Deep experience with generative AI and LLM-based application architectures (fine-tuning, prompt engineering, RAG, agentic frameworks).
- Strong knowledge of vector databases and semantic search (e.g. Pinecone, Weaviate, pgvector, Azure AI Search).
Data Engineering & Platforms
- Deep background in modern data engineering, ELT/ETL patterns, and large-scale data pipeline architectures.
- Hands-on experience with Snowflake (or equivalent cloud data warehouse) and associated data modelling patterns.
- Strong Azure ecosystem experience: Azure Data Factory, Azure Synapse, Azure OpenAI Service, and Azure Machine Learning.
Software Engineering & Architecture
- Strong software engineering fundamentals and the credibility to engage at technical depth with senior engineers.
- Experience with cloud-native, microservices, and event-driven architectures on Azure (or equivalent hyperscaler).
- Ability to make sound build vs buy vs integrate decisions across the AI and data tooling landscape.
Leadership & Business
- Proven experience building and leading high-performing Data / AI engineering teams.
- Experience within financial services, regtech, compliance, surveillance, or similarly regulated domains.
- Strong commercial instincts — the ability to connect technical investment to customer value and business outcomes.
Integrity and Ethics
All StarCompliance employees are expected to commit to a high standard of personal integrity and carry out their responsibilities in an ethical manner.
Originally posted on Himalayas