обновлено 25 дней назад
Staff Engineer (AI/Data)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff Engineer (AI/Data): Defining and governing data architecture across OLTP, OLAP, analytics, and AI foundations for a school management product suite with an accent on cloud-native distributed systems, DataOps, and AI-first engineering. Focus on architecting LLM orchestration, Retrieval-Augmented Generation, and real-time model inference at scale while influencing product and engineering direction across multiple teams.
Location: Remote
Company
develops MIS and school management tools used by more than 12,000 schools and trusts, with a data platform and AI-driven product features.
What you will do
- Define target architectures connecting transactional, analytical, and AI systems across Arbor’s product suite.
- Lead the end-to-end data lifecycle, including OLTP schema design, migrations, OLAP governance, platform engineering, and analytics.
- Guide Technical Leads on feature delivery, technical debt reduction, architecture improvements, and engineering quality.
- Translate product vision into technical deliverables and align commercial, product, and engineering goals.
- Architect infrastructure for LLM orchestration, Retrieval-Augmented Generation, and real-time model inference at scale.
- Mentor technical leaders, lead architectural reviews, and contribute to broader technology strategy, reliability, security, and compliance.
Requirements
- Extensive experience leading data architecture and delivering iterative outcomes across OLAP domains.
- Strong knowledge of data modelling, distributed cloud-native systems, and platforms such as Snowflake.
- Experience with unstructured data, DataOps, CI/CD for data, complex pipeline orchestration, AWS, Debezium, Git, and infrastructure as code.
- Ability to influence technical and non-technical stakeholders and support engineers and Technical Leads across distributed teams.
- Practical knowledge of data security, encryption, compliance, observability, performance, and SaaS platform cost optimisation.
- Understanding of AI-first engineering and experience contributing to technical communities of practice.
Nice to have
- AI/ML engineering and MLOps experience, including model deployment, serving, and monitoring.
- Experience with federated learning, privacy-enhancing technologies, or LLM operations.
- Prompt engineering at scale and token cost optimisation experience.
Culture & Benefits
- Flexible working arrangements for all roles.
- 32 days of holiday plus Bank Holidays.
- Wellbeing support, including a virtual GP, mental health support, counselling, and personalised health checks.
- Private dental insurance, pension, life assurance, and enhanced maternity, adoption, and paternity leave.
- Professional development budget, charity volunteering day, social committees, and dog-friendly offices.
Hiring process
- Phone screen.
- First-stage interview.
- Second-stage interview.
Visa sponsorship is not available.
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