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.