Overview

We are seeking an MLOps Engineer to build and maintain the infrastructure that powers our AI systems. You will bridge the data science and engineering teams, ensuring machine learning models are deployed, monitored, and scaled efficiently and reliably. This role covers the entire lifecycle of ML models in production, from automated deployment pipelines to performance and stability improvements. Ideal for a hands-on engineer who is passionate about robust, scalable, and automated ML systems, particularly for cutting-edge LLM-powered applications.

Base pay range

This range is provided by Opus 2. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Responsibilities

• Design, build, and maintain MLOps infrastructure, establishing CI/CD best practices for machine learning, including model testing, versioning, and deployment
• Develop and manage scalable pipelines for training, evaluating, and deploying ML models, with a focus on LLM-based systems
• Implement robust monitoring and logging for models in production to track performance, drift, and data quality, ensuring reliability and uptime
• Collaborate with Data Scientists to containerize and productionize models and algorithms, including RAG and Graph RAG approaches
• Manage and optimize cloud infrastructure for ML workloads on platforms like Amazon Bedrock or similar, focusing on performance, cost-effectiveness, and scalability
• Automate provisioning of ML infrastructure using Infrastructure as Code (IaC) principles and tools
• Work with product and engineering teams to integrate ML models into production environments and ensure seamless operation within the broader product architecture
• Own operational aspects of the AI lifecycle, from deployment and A/B testing to incident response and continuous improvement
• Contribute to AI strategy and roadmap by providing expertise on operational feasibility and scalability of proposed features
• Collaborate with Principal Data Scientists and Principal Engineers to ensure the MLOps framework supports the full scope of AI workflows

What excites us?

We have live AI features and a strong pipeline of customers. You will be part of a team that owns the entire lifecycle of these systems and ensures they are stable, scalable, and performant for users.

Qualifications

• 3+ years in an MLOps, DevOps, or Software Engineering role focused on machine learning infrastructure
• Experience with model lifecycle management and experiment tracking
• Ability to design infrastructure for complex AI systems, including vector stores and graph databases
• Proficiency in Python with experience in building and maintaining infrastructure and automation
• Experience with Java or TypeScript environments is beneficial
• Strong experience with at least one major cloud provider (AWS, GCP, Azure) and ML services (e.g., SageMaker, Vertex AI). Experience with Amazon Bedrock is a plus
• Familiarity with containerization (Docker) and orchestration (Kubernetes)
• Experience with Infrastructure as Code (Terraform, CloudFormation)
• Experience deploying and managing LLM-powered features in production
• Bonus: experience with monitoring tools (Prometheus, Grafana), agent orchestration, or legaltech domain knowledge

Benefits

• Contributory pension plan
• 26 days annual leave, hybrid working, and length of service entitlement
• Health Insurance
• Loyalty Share Scheme
• Enhanced Maternity and Paternity
• Employee Assistance Programme
• Electric Vehicle Salary Sacrifice
• Cycle to Work Scheme
• Mental well-being programs
• Volunteer leave
• Accessible and modern office space and regular company social events

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Information Technology

Industries

IT Services and IT Consulting

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