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Preply

Online language-learning marketplace connecting live tutors

Staff Machine Learning Ops Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeKyiv, UkraineHybrid

About the Job

Requirements

  • At least 9 years of engineering experience, with significant depth in large-scale machine learning, data, infrastructure, or platform systems.
  • Ability to architect and scale production-grade machine learning platforms supporting many teams, workflows, and use cases.
  • Deep understanding of cloud-native architecture and end-to-end machine learning workflows, including experimentation, feature management, model versioning, training, deployment, monitoring, performance benchmarking, and lifecycle management.
  • Hands-on experience with cloud platforms such as Google Cloud Platform or Amazon Web Services, Kubernetes, distributed computing, continuous integration and continuous delivery, observability, and infrastructure as code.
  • Experience building enabling tools and platform capabilities for Applied Scientists, Data Scientists, and engineering teams.
  • Strong technical judgment and ability to make pragmatic architecture decisions across reliability, scalability, security, cost, and developer experience.
  • Experience aligning cross-functional stakeholders and translating platform strategy into execution.
  • Ability to mentor engineers, raise engineering standards, and multiply the impact of teams.
  • Familiarity with large language model frameworks and generative AI infrastructure, such as LangChain, LlamaIndex, vector stores, retrieval systems, prompt evaluation, model serving, or large language model observability.

Responsibilities

  • Define the technical vision and roadmap for the machine learning platform to support growing machine learning and generative AI adoption across multiple teams, products, and business lines.
  • Lead the architecture of platform capabilities across the full machine learning lifecycle, including 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 continuous integration and delivery for machine learning, embedding testing, validation, security, performance checks, and release confidence into deployment pipelines.
  • Establish observability standards for machine learning systems, including model metrics, service health, alerts, drift detection, data quality, lineage, and business-impact monitoring.
  • Lead the evolution of generative AI and large language model platform capabilities, including building large language model 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 machine learning scientists and engineers move faster without compromising reliability or security.
  • Mentor senior engineers, influence architecture, raise standards, and guide teams through complex platform decisions.
  • Identify and eliminate bottlenecks in the path from machine learning research to production, making the platform easier, safer, and more efficient for machine-learning-powered product development.

About the Company

Preply

Online language-learning marketplace connecting live tutors

Preply is an online language-learning marketplace that connects learners with live tutors for 90+ languages across 180 countries. Through the platform, students can choose a language, browse tutors, schedule lessons, and receive video-based instruction from tutors around the world. Lessons are supported by an AI co-pilot toolkit that helps with practice, personalization, and learning data to aid the tutoring process. What sets Preply apart is its large, global network of more than 100,000 tutors, a broad language catalog, and a focus on combining human teaching with AI-assisted tools, backed by strong funding and profitability signals (it became EBITDA-positive) and a recent €127 million Series D to expand AI/data capabilities, product and engineering hiring, and global growth. The company’s goal is to make language learning accessible and effective for learners anywhere by scaling high-quality tutoring worldwide and enhancing it with AI-driven tools.

Company size
10,001+
Company stage
Series D
Total funding
$321.9M
Headquarters
Brookline, MA
Founded
2012

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