Machine Learning Ops Engineer

At Preply, we’re all about creating life-changing learning experiences. We help people discover the magic of the perfect tutor, craft a personalised learning journey, and stay motivated to keep growing. Our approach is human-led, tech-enabled - and it’s creating real impact.

We’ve just reached unicorn status with a $150M Series D, accelerating our vision to transform education through human-led, AI-enhanced learning. Today, 100,000+ tutors teach 90+ languages to learners in 180 countries - and we’re only getting started.

• Define the technical vision and roadmap for Preply’s ML platform, ensuring it can support growing ML and GenAI adoption across multiple teams, products, and business lines.
• Lead the architecture of platform capabilities across the full ML lifecycle: experimentation, feature engineering, artifact management, training, deployment, monitoring, retraining, and governance.
• Design cloud-native infrastructure for distributed training and inference, including GPU-based environments, autoscaling, workload isolation, rollout strategies, and cost optimization.
• Set the technical direction for CI/CD for ML, embedding testing, validation, security, performance checks, and release confidence into deployment pipelines.
• Establish observability standards for ML systems, including model metrics, service health, alerts, drift detection, data quality, lineage, and business-impact monitoring.
• Lead the evolution of Preply’s GenAI and LLM platform capabilities, including building LLM Gateway services, vector retrieval infrastructure, prompt experimentation, evaluation frameworks, latency-optimized inference, and reliable model-serving patterns.
• Partner with Applied Science and Data leads, Product leaders, and Engineering teams to align platform investments with experimentation velocity, cost efficiency, operational reliability, and user impact.
• Design platform abstractions, internal libraries, templates, and self-service tooling that help ML Scientists and engineers move faster without compromising reliability or security.
• Act as a technical multiplier across engineering by mentoring senior engineers, influencing architecture, raising standards, and guiding teams through complex platform decisions.
• Identify and eliminate bottlenecks in the path from ML research to production, making the platform easier, safer, and more efficient for all ML-powered product development.

• 9+ years of engineering experience, with significant depth in large-scale ML, data, infrastructure, or platform systems.
• Proven ability to architect and scale production-grade ML platforms that support many teams, workflows, and ML use cases.
• Deep understanding of cloud-native architecture and end-to-end ML workflows, including experimentation, feature management, model versioning, training, deployment, monitoring, performance benchmarking, and lifecycle management.
• Strong hands-on experience with cloud platforms such as 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 and the ability to make pragmatic architecture decisions across reliability, scalability, security, cost, and developer experience.
• Excellent communication and influence skills, with experience aligning cross-functional stakeholders and translating platform strategy into execution.
• Demonstrated ability to mentor engineers, raise engineering standards, and multiply the impact of teams around you.
• Product-impact mindset: you care about building platform capabilities that accelerate experimentation, improve user experiences, and unlock measurable business value.
• Familiarity with LLM frameworks and GenAI infrastructure.

• An open, collaborative, dynamic and diverse culture.
• A generous monthly allowance for lessons on Preply.com, Learning & Development budget and time off for your self-development.
• A competitive financial package with equity, leave allowance and health insurance.
• Access to free mental health support platforms.
• The opportunity to shape the lives of learners and tutors through language learning and teaching in 175 countries (and counting!).