7 дней назад
London - ML Ops Engineer II (Experiences)
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Описание вакансии
Текст:
TL;DR
London - ML Ops Engineer II (Experiences) (AWS/ML Platforms): Building and maintaining scalable infrastructure for pre-computed, batch, and real-time machine learning models with an accent on cloud platforms, model lifecycle support, and reliable deployment. Focus on monitoring ML pipelines, developing infrastructure as code, supporting high-throughput and low-latency workloads, and enabling data science teams to deliver AI products.
Location: Hybrid role based in London, United Kingdom; candidates must already be based in London or within a maximum 1.5-hour commute. Office attendance is approximately twice per month.
Company
, part of Tripadvisor, operates a global marketplace for travel experiences and uses technology, AI, machine learning, and data science to connect travelers with experiences.
What you will do
- Provide tools, infrastructure, and support for data science and machine learning stakeholders.
- Develop and evolve the MLOps technology stack in AWS cloud environments.
- Build and maintain scalable infrastructure for pre-computed, batch, and real-time model workloads.
- Own software engineering activities from design and implementation through QA, monitoring, and maintenance.
- Monitor ML pipelines for accuracy, drift, enhancements, SLA compliance, and billing volumes.
- Define and document requirements with cross-functional stakeholders for ML and AI products.
Requirements
- At least 2 years of MLOps experience building infrastructure across the machine learning model lifecycle.
- Hands-on experience with AWS and/or GCP.
- Experience with infrastructure-as-code tools such as Terraform or CloudFormation.
- Experience with CI/CD processes and platforms.
- Ability to work across diverse technologies and collaborate effectively in cross-functional teams.
- Strong verbal and written communication, ownership, urgency, and attention to quality.
Nice to have
- Experience with Kubernetes, Seldon, KServe, Ray Serve, MLflow, SageMaker, Kubeflow, Argo CD, Docker, Python, Java, Ray, Spark, Pandas, PostgreSQL, Snowflake, or BigQuery.
- Knowledge of vector or graph databases.
- Experience optimizing models for high throughput, low latency, and cost efficiency.
- Experience with LLMOps and open-source models.
Culture & Benefits
- Remote-friendly collaboration with flexible working arrangements and the option to work onsite in select locations.
- Flexible schedule designed to support work-life balance.
- Competitive compensation including base salary, annual bonus, and equity.
- Health benefits, employee assistance, donation matching, tuition assistance, and an annual lifestyle benefit.
- Travel discounts and an inclusive workplace focused on curiosity, collaboration, customer service, and continuous improvement.
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