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# Staff Machine Learning Ops Engineer

- **Company:** Preply Inc.
- **Location:** Barcelona, Spain
- **Contract:** Permanent contract
- **Skills:** Big Data, Cloud Computing, Continuous Integration, Distributed Computing Environment, Machine Learning, Cloud Services, Azure Machine Learning, Management of Software Versions, Autoscaling, Large Language Models, Machine Learning Operations, GPT
- **Published:** September 16, 2026
- **Apply:** https://www.buscojobs.com.es/staff-machine-learning-ops-engineer-en-barcelona-ID-372137207

## About the Role

Requisitos principales8+ years of engineering experience in large-scale Data/ML platforms
Deep knowledge of cloud services and end-to-end ML workflows (versioning, monitoring, performance benchmarking)
Experience collaborating with scientists and building enabling tools
Excellent communication and cross-functional influence; mentoring ML engineers
Familiarity with LLM frameworks (LangChain, LlamaIndex), vector stores, and retrieval infrastructure
communication and influence
mentoring and coaching
cross-functional collaboration
cloud-native ML platform design
distributed training and inference on cloud
CI/CD for ML

## Description

OverviewIn this role you will architect and evolve Preply's ML platform to move research into production at scale.You'll design cloud-native systems for distributed training and inference, with strong emphasis on observability, testing, and cost-efficiency.You will partner with ML leads and engineers to set standards, enable experimentation, and deliver modular, measurable ML services.This position offers impact across product teams and a chance to shape the future of a global learning platform.Compensaciones / Beneficioslesson allowance (monthly)Learning & Development budgettime off for self-developmentequityhealth insurancemental health support platformsResponsabilidadesDesign and implement the ML platform architecture (experiment tracking, artifact management, scalable deployment)Build cloud-native distributed training and inference solutions with GPU support and autoscalingOwn CI/CD for ML, including testing, validation, and performance checksEmbed observability across ML workflows (metrics, alerts, drift detection, lineage)Mentor engineers, influence standards, and reduce risk in complex decisionsAlign platform direction with experimentation velocity, cost-efficiency, and user impactDevelop modular, testable ML services with monitoring from day oneContribute to LLM platform capabilities (RAG pipelines, latency-optimized inference, prompt experimentation frameworks)Requisitos principales8+ years of engineering experience in large-scale Data/ML platformsDeep knowledge of cloud services and end-to-end ML workflows (versioning, monitoring, performance benchmarking)Experience collaborating with scientists and building enabling toolsExcellent communication and cross-functional influence; mentoring ML engineersFamiliarity with LLM frameworks (LangChain, LlamaIndex), vector stores, and retrieval infrastructurecommunication and influencementoring and coachingcross-functional collaborationcloud-native ML platform designdistributed training and inference on cloudCI/CD for ML


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