MLOps Engineer

Booksy

Job Overview

Location

Remote

Employment Type

Full-time

Work Arrangement

Remote

Sector

Information Technology & Software

Experience Level

Senior (5-8 years)

Application Deadline

July 6, 2026

About the Company

Booksy is a global leader dedicated to empowering beauty professionals and simplifying the client appointment experience. Our mission is to help people around the world feel great about themselves by connecting them with their beauty and wellness services seamlessly.

We operate as a fast-moving scale-up, fostering an environment where innovation thrives. Our platform is designed to handle the intricate aspects of running a beauty business, freeing up professionals to focus on their craft. We believe in the power of technology to help entrepreneurs build successful businesses and support customers in managing their 'me time' moments.

At Booksy, we are building a new Data Science & Applied AI function. This team is crucial for developing and deploying intelligent systems that drive our business forward. We are committed to creating a diverse and inclusive workplace where all employees feel valued and have equal opportunities.

Job Description

Join Booksy as a foundational Senior ML/MLOps Engineer and play a key role in shaping our new Data Science & Applied AI function. You will partner closely with Data Scientists to transition intelligent systems from prototype to production, ensuring their ongoing performance and scalability.

This role offers the unique opportunity to own the end-to-end productionization of machine learning models. You will be responsible for real-time serving, establishing standardized CI/CD pipelines, deploying GenAI and RAG systems, and implementing robust monitoring infrastructure to detect drift and quality degradation.

Leveraging a GCP-native stack including Vertex AI, BigQuery, dbt, Airflow, and Terraform, you will tackle impactful problems from day one. You will have the autonomy to influence how the function scales and contribute to future hiring decisions.

Collaboration is key; you will work closely with Data Scientists, Platform, and Engineering teams, acting as both a technical owner and an advocate for Data Science needs across the organization.

To apply for this role, click the Apply button on this page and follow the instructions.

Required Skills

MLOpsMachine LearningPythonSQLGCPVertex AIKubeflowBigQueryCI/CDModel ServingFeature StoresAutoscalingBlue/Green DeploymentsProduction MonitoringModel RegistriesVersioningDrift DetectionAutomated RetrainingGenAIRAG SystemsLLM EvaluationObservability ToolingCost ManagementLatency ManagementEnglish Language

Key Responsibilities

Own the productionization of machine learning models end-to-end, including real-time serving against SLAs.
Establish and maintain a standardized CI/CD path to production for machine learning models.
Deploy GenAI and RAG systems.
Implement monitoring infrastructure to detect model drift and quality degradation.
Collaborate closely with Data Scientists, Platform, and Engineering teams.
Act as a technical owner and advocate for Data Science needs.
Shape the scaling of the Data Science & Applied AI function and contribute to hiring decisions.

Qualifications

A track record of putting models into production alongside Data Scientists (classic ML, LLM/GenAI, or both).
Deep understanding of the ML lifecycle from prototype to production, including real-time serving, feature stores, autoscaling, and blue/green deployments.
Strong Python and SQL skills.
Hands-on GCP experience, especially Vertex AI, Kubeflow pipelines, and BigQuery, or strong fluency on an equivalent cloud with a clear ability to adapt quickly.
Solid CI/CD and production-monitoring experience, including model registries, versioning, drift detection, and automated retraining.
Conversational-level English language skills.

Benefits & Perks

Fully remote position across the UK, Spain, and Poland.
Competitive salary plus bonus.
Private health cover, life insurance, pension, and enhanced parental leave (tailored to location).
Generous holiday allowance plus public holidays.
Access to a global learning and development program.
Wellbeing support.
Discounts across partner platforms.

How to Apply

Kindly submit your application and CV in English.

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The machine learning landscape is rapidly expanding, with MLOps engineers playing a pivotal role in operationalizing AI models. This position focuses on bridging the gap between data science and production environments, ensuring the seamless deployment and maintenance of machine learning systems. Key technical areas include CI/CD pipelines, real-time model serving, and MLOps best practices. The impact of this role is significant, directly influencing business ROI through efficient model deployment and scalability, and shaping the future of applied AI within the organization.