Senior MLOps Engineer - Personalisation

Bynd Limited; United Kingdom; On-site; GBP 70,000 - 90,000; Full time

Job summary

A technology consultancy is seeking a Senior MLOps Engineer to lead the automation and operational excellence of machine learning systems. Responsibilities include managing ML lifecycles, building CI/CD pipelines, and collaborating with data teams. Ideal candidates will have extensive MLOps experience, proficiency in GCP tools, and a strong background in Python and observability frameworks.

Qualifications

• 7+ years of deep, hands-on experience in MLOps or DevOps focused on machine learning.
• Proven experience building MLOps frameworks with clear improvement examples.
• Expert-level knowledge of GCP cloud stack, particularly Vertex AI and BigQuery.

Responsibilities

• Own and evolve the end-to-end ML lifecycle from data ingestion to model serving.
• Design and manage automated CI/CD pipelines for ML models.
• Implement observability frameworks for production ML models.

Skills

MLOps experience

GCP expertise (Vertex AI)

CI/CD pipeline management

Python proficiency

Containerization (Docker/Kubernetes)

IaC tools (Terraform/Ansible)

Observability stacks knowledge

Education

BSc, MSc, or PhD in Computer Science, Engineering, or related field

Tools

Terraform

Prometheus

Grafana

ELK

Datadog

Kubeflow

MLflow

Job description

Senior MLOps Engineer - Personalisation

Join to apply for the Senior MLOps Engineer - Personalisation role at Beyond.

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.

Role Overview

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. You will take existing pipelines and evolve them to be best‑in‑class, responsible for operationalising new models (such as ranking, NBA, and LLM‑based solutions) with agility and efficiency. Your primary goal is to create a seamless, reliable, and highly observable environment on GCP that empowers our Data Scientists and ML Engineers to iterate and deploy models faster. You will be expected to have created or significantly evolved MLOps frameworks in the past and be able to quantify the improvements you deliver (e.g., in deployment frequency, model performance monitoring, or system reliability).

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, implementing 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 their workflow.
• 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 such as BigQuery and Oracle.
• Define and track key MLOps metrics to quantify and communicate improvements in system performance, model quality, and team velocity.

What We’re Looking For

• 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 the improvements you delivered.
• Expert‑level knowledge of the GCP cloud stack, particularly Vertex AI, BigQuery, Pub/Sub, and GKE.
• Deep expertise in building and managing observability stacks for real‑time ML systems (e.g., Prometheus, Grafana, ELK).
• Proven experience operationalising LLM‑based systems, including managing embedding generation pipelines and fine‑tuning workflows.
• Strong practical experience with IaC tools such as Terraform or Ansible.
• Demonstrable expertise in building and managing complex CI/CD pipelines.
• Proficiency in Python and experience scripting for automation and tooling.
• Strong understanding of containerisation (Docker, Kubernetes) and microservices architecture for ML model serving.

Nice to Have

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

Our Culture & Commitment

Having been named among the Sunday Times Best 100 Companies, we believe 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.

Beyond is committed to welcoming everyone, regardless of gender identity, orientation, or expression. Our mission is to remove exclusivity and barriers and encourage new thinking and perceptions in a space of belonging. It is not about race, gender, or age, it is about people. And without our people being their most creative and innovative selves, we are nothing.

Job Details

Seniority level: Mid‑Senior level

Employment type: Full‑time

Job function: IT Services and IT Consulting