Machine Learning Engineer - Hybrid

Social network you want to login/join with: Machine Learning Ops Engineer - AI, London EU work permit required: As Opus 2 continues to embed AI into our platform, we need robust, scalable data systems that power intelligent workflows and support advanced model behaviours. We’re looking for an MLOps Engineer to build and maintain the infrastructure that powers our AI systems. You will be the bridge between our data science and engineering teams, ensuring that our machine learning models are deployed, monitored, and scaled efficiently and reliably. You’ll be responsible for the entire lifecycle of our ML models in production, from building automated deployment pipelines to ensuring their performance and stability. This role is ideal for a hands-on engineer who is passionate about building robust, scalable, and automated systems for machine learning, particularly for cutting-edge LLM-powered applications. Design, build, and maintain our MLOps infrastructure, establishing best practices for CI/CD for machine learning, including model testing, versioning, and deployment. Develop and manage scalable and automated pipelines for training, evaluating, and deploying machine learning models, with a specific focus on LLM-based systems. Implement robust monitoring and logging for models in production to track performance, drift, and data quality, ensuring system reliability and uptime. Collaborate with Data Scientists to containerize and productionize models and algorithms, including those involving RAG and Graph RAG approaches. Manage and optimize our cloud infrastructure for ML workloads on platforms like Amazon Bedrock or similar, focusing on performance, cost-effectiveness, and scalability. Automate the provisioning of ML infrastructure using Infrastructure as Code (IaC) principles and tools. Work closely with product and engineering teams to integrate ML models into our production environment and ensure seamless operation within the broader product architecture. Own the operational aspects of the AI lifecycle, from model deployment and A/B testing to incident response and continuous improvement of production systems. Contribute to our AI strategy and roadmap by providing expertise on the operational feasibility and scalability of proposed AI features. Collaborate closely with Principal Data Scientists and Principal Engineers to ensure that the MLOps framework supports the full scope of AI workflows and model interaction layers. We have live AI features and a strong pipeline of customers excited to get access to more improved AI-powered workflows. Our focus is on delivering real, valuable AI-powered features to customers and doing it responsibly. You have hands-on experience building and managing CI/CD pipelines for machine learning. You're comfortable writing production-quality code, reviewing PR's, and are dedicated to delivering a reliable and observable production environment. Ability to reason about and implement infrastructure for complex AI systems, including those leveraging vector stores and graph databases. ~ Proven ability to ensure the performance and reliability of systems over time. ~3+ years of experience in an MLOps, DevOps, or Software Engineering role with a focus on machine learning infrastructure. ~ Proficiency in Python, with experience in building and maintaining infrastructure and automation, not just analyses. ~ Experience working in Java or TypeScript environments is beneficial. ~ Deep experience with at least one major cloud provider (AWS, GCP, Azure) and their ML services (e.G., SageMaker, Vertex AI). Bonus : experience with monitoring tools (e.G., Opus 2 is a global leader in legal software and services, trusted partner of the world’s leading legal teams. All our achievements are underpinned by our unique culture where our people are our most valuable asset. Contributory pension plan. ~26 days annual holidays, hybrid working, and length of service entitlement. ~ Health Insurance. ~ Loyalty Share Scheme. ~ Employee Assistance Programme. ~ Electric Vehicle Salary Sacrifice. ~ Cycle to Work Scheme. ~ Accessible and modern office space and regular company social events. #