Senior MLOps Engineer - Personalisation

Beyond; United Kingdom; On-site; GBP 80,000 - 100,000; Full time

Job summary

A technology consultancy in the United Kingdom seeks an experienced Senior MLOps Engineer to oversee the automation and operational excellence of machine learning systems. The ideal candidate will have over 7 years of hands-on experience in MLOps, showcasing expertise in GCP and the development of CI/CD pipelines. Join a diverse and inclusive environment that values innovation and collaboration.

Qualifications

• 7+ years in MLOps or DevOps focused on machine learning systems.
• Experience building MLOps frameworks with documented improvements.
• Expertise in GCP, especially Vertex AI, BigQuery, and GKE.

Responsibilities

• Own and evolve the ML lifecycle from data ingestion to deployment.
• Design and manage automated CI/CD pipelines for ML models.
• Implement observability framework for monitoring ML models in production.

Skills

MLOps frameworks

GCP cloud stack

Observability stacks

Infrastructure as Code

Python scripting

Containerisation (Docker, Kubernetes)

Education

BSc, MSc, or PhD in Computer Science or Engineering

Tools

Terraform

Prometheus

Grafana

ELK stack

Job description

Role Overview

Beyond is a technology consultancy helping organizations thrive in a rapidly changing world. We build, modernize, scale, and operationalize technology, creating Cloud and AI solutions to unlock productivity and drive customer growth.

We’re looking for a highly experienced Senior MLOps Engineer to own the automation, scaling, and operational excellence of our machine learning systems. This role is the critical bridge between our data science/ML engineering teams and a high‑availability production environment.

What You’ll Do

• Take ownership of and evolve our end‑to‑end ML lifecycle, from data ingestion and feature engineering pipelines to model training, deployment, and real‑time serving.
• Design, build, and manage robust, automated CI/CD/CT pipelines specifically for ML models, integrating with existing CI/CD patterns.
• Leverage the GCP ecosystem, especially Vertex AI Pipelines, Vertex AI Endpoints, and Vertex AI Model Registry, to create a standardised and efficient path to production.
• Design and own a best‑in‑class observability framework for ML models in production, including granular monitoring for model performance, data and concept drift, and operational health.
• Collaborate closely with Data Scientists and ML Engineers to understand their needs and build tools that accelerate workflows.
• Optimise ML serving infrastructure for low‑latency, real‑time personalisation requirements.
• Partner with data engineering to ensure robust integration with feature stores and data sources (e.g., BigQuery and Oracle).
• Define and track key MLOps metrics to quantify and communicate improvements in system performance, model quality, and team velocity.

Qualifications

• 7+ years of deep, hands‑on experience in a dedicated MLOps or DevOps role focused on machine learning systems.
• Proven experience building or evolving MLOps frameworks from the ground up, with clear examples of delivered improvements.
• Expert‑level knowledge of the GCP cloud stack, particularly Vertex AI (Pipelines, Endpoints, Training), BigQuery, Pub/Sub, and GKE.
• Deep expertise in building and managing observability stacks for real‑time ML systems (e.g., Prometheus, Grafana, ELK stack).
• Proven experience operationalising LLM‑based systems, including embedding generation pipelines, vector databases, and fine‑tuning/deployment workflows.
• Strong practical experience with Infrastructure as Code tools (e.g., Terraform, Ansible).
• Demonstrable expertise in building and managing complex CI/CD pipelines.
• Proficiency in Python and experience with scripting for automation and tooling for ML teams.
• Strong understanding of containerisation (Docker, Kubernetes) and microservices architecture as it applies to ML model serving.

Nice to Have

• Relevant Google Cloud certifications (e.g., Professional Machine Learning Engineer, Professional Cloud DevOps Engineer).
• BSc, MSc, or PhD in Computer Science, Engineering, or a related technical field.
• Hands‑on experience with Datadog for monitoring ML systems and cloud infrastructure.
• Familiarity with the deployment challenges of ranking, recommendation, or NBA models.
• Experience with other ML platforms or tools (e.g., Kubeflow, MLflow).
• Knowledge of networking and security principles within GCP.

Our Commitment to Diversity

Beyond believes culture plays a large role in what we offer as an organization. We actively promote diversity in all its forms across our studios, and we proudly, passionately, and proactively strive to create a culture of inclusivity and openness for all our employees. We are committed to welcoming everyone, regardless of gender identity, orientation, or expression, and we value people above all else.