2 months ago
London, United KingdomStaff+
Responsibilities
- Define Preply’s technical vision and roadmap for its ML and GenAI platform.
- Lead architecture across the full ML lifecycle, including experimentation, feature engineering, artifact management, training, deployment, monitoring, retraining, and governance.
- Design cloud-native infrastructure for distributed training and inference, including GPU environments, autoscaling, workload isolation, rollout strategies, and cost optimization.
- Set technical direction for ML CI/CD with testing, validation, security, performance checks, and release controls.
- Establish observability standards covering model metrics, service health, alerts, drift detection, data quality, lineage, and business impact.
- Develop GenAI and LLM platform capabilities such as LLM gateways, vector retrieval infrastructure, prompt experimentation, evaluation frameworks, and model-serving patterns.
- Build platform abstractions, internal libraries, templates, and self-service tooling for ML Scientists and engineers.
- Partner with Applied Science, Data, Product, and Engineering leaders to align platform investments with experimentation velocity, reliability, cost efficiency, and user impact.
- Mentor senior engineers, influence architecture, raise engineering standards, and guide complex platform decisions.
Requirements
- 9+ years of engineering experience with significant depth in large-scale ML, data, infrastructure, or platform systems.
- Experience architecting and scaling production-grade ML platforms for multiple teams, workflows, and use cases.
- Deep understanding of cloud-native architecture and end-to-end ML workflows, including experimentation, feature management, model versioning, training, deployment, monitoring, benchmarking, and lifecycle management.
- Strong hands-on experience with GCP or AWS, Kubernetes, distributed compute, CI/CD, observability, and infrastructure-as-code practices.
- Experience building enabling tools and platform capabilities for Applied Scientists, Data Scientists, and engineering teams.
- Strong technical judgment across reliability, scalability, security, cost, and developer experience.
- Experience aligning cross-functional stakeholders and translating platform strategy into execution.
- Demonstrated ability to mentor engineers, raise engineering standards, and multiply team impact.
- Product-impact mindset focused on experimentation, user experience, and measurable business value.
- Familiarity with LLM frameworks and GenAI infrastructure, including LangChain, LlamaIndex, vector stores, retrieval systems, prompt evaluation, model serving, or LLM observability.
Benefits
- Open, collaborative, dynamic, and diverse culture.
- Monthly allowance for lessons on Preply.com.
- Learning and Development budget and time off for self-development.
- Competitive financial package including equity, leave allowance, and health insurance.
- Access to free mental health support platforms.
- Opportunity to support learners and tutors through language learning and teaching in 175 countries.
Tech Stack
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