Senior MLOps Engineer in London

Wood Mackenzie is the global leader in analytics, insights and proprietary data across the entire energy and natural resources landscape. For over 50 years our work has guided the decisions of the world’s most influential energy producers, utilities companies, financial institutions and governments. Now, with the world’s energy system more complex and interconnected than ever before, sector-specific views are no longer enough. That’s why we’ve redefined what’s possible with Intelligence Connected. By fusing our unparalleled proprietary data with the sharpest analytical minds, all supercharged by Synoptic AI, we deliver a clear, interconnected view of the entire value chain. Our trusted team of 2,700 experts across 30 countries breaks siloes and connects industries, markets and regions across the globe. This empowers our customers to identify risk sooner, spot opportunities faster and recalibrate strategy with confidence – whether planning days, weeks, months or decades ahead.

Main responsibilities

• Design, build, and maintain highly scalable, robust, and secure machine learning infrastructure and platforms across the entire organization.
• Define and drive the long-term MLOps vision, roadmap, and best practices in alignment with broader business and engineering goals.
• Establish and optimize automated CI/CD/CT pipelines for machine learning models, ensuring seamless transitions from research to production.
• Oversee the deployment of complex models (including LLMs and deep learning models), optimizing for latency, throughput, and cost-efficiency.
• Implement enterprise-grade monitoring, alerting, and logging for model performance, data drift, concept drift, and system health. Ensure robust AI governance and security compliance.
• Partner closely with Data Scientists, Data Engineers, Software Engineers, and Product Managers to bridge the gap between model development and software engineering, developing standardised workflows that accelerate the path to production.
• Mentor data scientists in MLOps best practices, foster a culture of engineering excellence, and lead technical design reviews.

Key Skills & Experience

• Extensive Experience: considerable experience in software engineering, DevOps, or Data Engineering, with dedicated experience in MLOps, ML infrastructure, or deploying ML models at scale.
• Cloud & Infrastructure: Deep, hands-on expertise with AWS and its respective managed ML/AI services (SageMaker, Bedrock).
• Containerization & Orchestration: Advanced proficiency with Kubernetes, Docker, and ML-specific orchestration tools like MLFlow.
• Programming Languages: Strong software development skills in Python, alongside proficiency in languages like C++, or Java for high-performance systems.
• CI/CD & Infrastructure as Code: Mastery of automation tools (GitHub Actions, GitLab CI, Jenkins, Octopus Deploy) and IaC frameworks (Terraform, Pulumi, Ansible).
• ML Framework Knowledge: Strong understanding of the underlying mechanics of popular ML and deep learning frameworks (PyTorch, TensorFlow, Scikit-Learn) to effectively troubleshoot and optimize deployments.
• Leadership Track Record: Demonstrated ability to lead complex, multi-quarter technical initiatives from conception to successful production rollout, including stakeholder management.

We are an equal opportunities employer. This means we are committed to recruiting the best people regardless of their race, colour, religion, age, sex, national origin, disability or protected veteran status.