MLOps Engineer

What we’re all about. 

Do you ever have the urge to do things better than the last time? We do. And it’s this urge that drives us every day. Our environment of discovery and innovation means we’re able to create deep and valuable relationships with our clients to create real change for them and their industries. It’s what got us here – and it’s what will make our future. At Quantexa, you’ll experience autonomy and support in equal measures allowing you to form a career that matches your ambitions. 41% of our colleagues come from an ethnic or religious minority background. We speak over 20 languages across our 47 nationalities, creating a sense of belonging for all.

Role Overview 

Founded in 2016 by a small team, Quantexa was built with a vision of enabling better decision making through better data-driven intelligence. Seven years, twelve locations and 700+ employees later we recently gained “Unicorn” status with our Series E funding round.  

Our Analytics teams build, deploy and maintain the a wide range of AI models which underpin our platform. This includes specific expertise in emerging methods for Graph based model and NLP models. Our MLOps team is task with automating and maximising efficiency of the build, deployment and maintenance of all model types.  

We are looking for a highly skilled MLOps Engineer to join our overseas team, working in parallel with our existing MLOps team. This individual will focus on developing and maintaining the infrastructure and automation pipelines that support our machine learning models, ensuring that they can be deployed efficiently into production environments. 

This role will involve close collaboration with data scientists, data engineers, and other MLOps engineers to deliver robust, scalable machine learning pipelines that are optimized for production environments. 

Requirements

Model Deployment 

• Collaborate with data scientists to ensure smooth deployment of machine learning models into production environments. 

• Automate the deployment of machine learning models using CI/CD pipelines and container orchestration tools like Kubernetes and Docker. 

• Ensure proper model versioning, governance, and compliance using tools like MLFlow, Kubeflow, or DVC. 

Pipeline Automation 

• Build and maintain data and model pipelines for training, validation, deployment, and monitoring. 

• Develop automated processes for data validation, feature engineering, and model training. 

• Integrate pipelines with distributed data processing frameworks (e.g., Spark, Kafka) to ensure efficient data handling for model training and inference. 

Monitoring & Maintenance 

• Set up monitoring systems to track model performance and detect issues like model drift, triggering retraining when necessary. 

• Work with cloud infrastructure to scale models and ensure high availability in production environments. 

• Contribute to the troubleshooting and resolution of issues with models in production. 

Collaboration 

• Work closely with data engineers and senior MLOps engineers to ensure consistency across teams and smooth integration of machine learning pipelines. 

• Participate in Scrum ceremonies, ensuring timely delivery of new features and improvements. 

Required Skills & Experience 

MLOps Expertise 

• Experience deploying machine learning models into production and managing the full model lifecycle. 

• Good knowledge of the machine learning development lifecycle, with experience building a project end-to-end 

• Good understanding of NLP  

• Hands-on experience with MLOps tools like MLFlow, Kubeflow, DVC, or equivalent. 

• Experience with containerization technologies, including Docker and Kubernetes. 

• Experience with Data Engineering  

Automation & Infrastructure 

• Experience with CI/CD tools such as Jenkins, GitLab CI, or similar for automating model deployment. 

• Proficiency in cloud platforms. 

• Experience with infrastructure as code tools such as Terraform and Helm for managing cloud-based infrastructure. 

Programming & Frameworks 

• Proficiency in Python, including experience with machine learning libraries like PyTorch, TensorFlow, or Scikit-learn. 

• Experience developing and working with REST APIs. 

• Good knowledge of Spark and/or Java. 

• Familiarity with distributed data processing frameworks like Spark, Kafka, or similar. 

• Strong knowledge of BDD/TDD, and general testing principles. 

Preferred Experience 

• Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) for tracking production models. 

• Familiarity with GPUs and deploying models optimized for specialized hardware. 

• Knowledge of feature stores, embeddings, and working with large-scale machine learning models. 

Benefits

We offer:

• Competitive salary 💰
• Company bonus
• Free Calm App Subscription #1 app for meditation, relaxation and sleep 🧘‍♀️
• Ongoing personal development
• Great Company wide socials
• Plus other local benefits

Our mission

We have one mission. To help businesses grow. To make data easier. And to make the world a better place. We’re not a start-up. Not anymore. But we’ve not been around that long either. What we are is a collection of bright, passionate minds harnessing complexities and helping our clients and their communities. One culture, made of many. Heading in one direction – the future.

It's all about you

Quantexa is proud to be an Equal Opportunity Employer. We’re dedicated to creating an inclusive and diverse work environment, where everyone feels welcome, valued, and respected. We want to hear from people who are passionate about their work and align with our values. Qualified applications will receive consideration for employment without regard to their race, colour, ancestry, religion, national origin, sex, sexual orientation, gender identity, age, citizenship, marital, disability, or veteran status. Whoever you are, if you’re a curious, caring, and authentic human being who wants to help push the boundaries of what’s possible, we want to hear from you.

Internal pay equity across departments is crucial to our global compensation philosophy. Grade level and salary ranges are determined through interviews and a review of experience, education, training, knowledge, skills, and abilities of the applicant, equity with other team members, and alignment with market data.

Quantexa is committed to providing reasonable accommodations in our talent acquisition processes. If you require support, please inform our Talent Acquisition Team.