Staff Machine Learning Ops Engineer

Preply is scaling an AI-powered learning platform used by millions of learners and tutors worldwide.

As ML and GenAI become increasingly central to our product, marketplace, and operational systems, we’re looking for a Staff ML Ops Engineer to define and evolve the platform that enables machine learning at company scale.

This is a senior technical leadership role for an engineer who can combine deep hands‑on expertise with platform vision. You will help define the current and future direction of Preply’s ML platform, set technical standards, and design the systems that allow ML teams to move from research to production quickly, safely, and cost‑efficiently.

You’ll partner closely with Applied Science, Data, Product, and Engineering leadership to build a scalable, secure, and observable ML platform that powers multiple business lines. Your impact will come not only from the systems you design, but from the standards you set, the engineers you mentor, and the technical decisions you help de‑risk across the organization.

Why is this role important:

Preply’s next stage of growth depends on making AI a scalable company‑wide capability, not a collection of isolated models and experiments. This role will define how ML systems are built, deployed, monitored, and improved across the company.

You’ll shape the platform that powers personalized learning, smarter experiences for tutors and learners, marketplace intelligence, content generation, automation, and future GenAI products. The goal is to make it dramatically easier for teams to turn ML ideas into reliable, secure, cost‑efficient product experiences at a global scale.

What you’ll be doing:

• 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.

What you need to succeed:

• 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, such as LangChain, LlamaIndex, vector stores, retrieval systems, prompt evaluation, model serving, or LLM observability.

Why you’ll love it at Preply

• 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 unlock the potential of learners and tutors through language learning and teaching in 175 countries.

Diversity, Equity, and Inclusion

Preply.com is committed to creating an inclusive environment where people of diverse backgrounds can thrive. We believe that the presence of different opinions and viewpoints is a key ingredient for our success as a multicultural Ed-Tech company. That means that Preply will consider all applications for employment without regard to race, color, religion, gender identity or expression, sexual orientation, national origin, disability, age or veteran status.

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