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.
Wood Mackenzie
Intelligence Connected
WoodMac.com
Wood Mackenzie Brand Video
Wood Mackenzie Values
- Inclusive – we succeed together
- Trusting – we choose to trust each other
- Customer committed – we put customers at the heart of our decisions
- Future Focused – we accelerate change
- Curious – we turn knowledge into action
As a Principal MLOps Engineer, you will be the architectural visionary driving the design, scalability, and security of our enterprise machine learning infrastructure.
You will lead the technical strategy for end-to-end ML lifecycles, establishing robust automated pipelines, optimizing complex model serving, and ensuring rigorous observability across all production deployments.
In this highly cross-functional role, you will partner closely with data science and engineering teams to bridge the gap between research and production, while simultaneously mentoring talent and setting the standard for engineering excellence.
Main responsibilities
Working in the central machine learning department, you will be collaborating with our data science and engineering teams and reporting to the VP of Machine Learning.
Responsibilities will include:
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
You will be passionate about solving complex customer problems and bringing great products to market.
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.
Equal Opportunities
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. You can find out more about your rights under the law at www.eeoc.gov
If you are applying for a role and have a physical or mental disability, we will support you with your application or through the hiring process.
