Senior MLOps Engineer – Personalisation

Job not on LinkedIn

October 31

Apply Now

Receive Emails with Similar Jobs

Beyond

WebsiteLinkedInAll Job Openings

Artificial Intelligence • Cloud • SaaS

Beyond is a company that provides a range of cloud services and solutions focused on enhancing data, artificial intelligence, and cloud initiatives. They are known for their expertise in Generative AI, Machine Learning, and MLOps, working with partners like Google Cloud to deliver high-quality solutions. Beyond offers services such as cloud implementation, operations support, and AI-driven workplace solutions. They also focus on improving customer experiences and creating innovative technology products. Beyond has a global presence, delivering solutions in regions including the US, UK, Ireland, and Europe, and they are a trusted Google Cloud partner.

51 - 200 employees

🤖 Artificial Intelligence

☁️ SaaS

📋 Description

• 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 (Continuous Integration / Continuous Delivery / Continuous Training) 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. This includes implementing granular monitoring for model performance (accuracy, bias), data and concept drift, and operational health (latency, throughput, error rates). • Collaborate closely with Data Scientists and ML Engineers to understand their needs, building the tools and abstractions that create a seamless environment and 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 (like BigQuery and Oracle). • Define and track key MLOps metrics to quantify and communicate improvements in system performance, model quality, and team velocity.

🎯 Requirements

• 7+ years of deep, hands-on experience in a dedicated MLOps or DevOps role with a strong focus 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 (Pipelines, Endpoints, Training), BigQuery, Pub/Sub, and GKE. • Deep expertise in building and managing observability stacks for real-time ML systems (e.g., using tools like Prometheus, Grafana, ELK stack, or specialised platforms). • Proven experience operationalising LLM-based systems, including managing embedding generation pipelines, vector databases, and fine-tuning/deployment workflows. • Strong practical experience with Infrastructure as Code (IaC) tools (e.g., Terraform, Ansible). • Demonstrable expertise in building and managing complex CI/CD pipelines. • Proficiency in Python and experience with scripting for automation, infrastructure management, and building tooling for ML teams. • Strong understanding of containerisation (Docker, Kubernetes) and microservices architecture as it applies to ML model serving.