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. 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 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 tasked with automating and maximizing efficiency of the build, deployment and maintenance of all model types.
We are seeking a senior MLOps Engineer to join our team. This individual will play a crucial role in designing, deploying, and maintaining production-level machine learning models. The Senior MLOps Engineer will focus on leading MLOps initiatives, including infrastructure, automation, and ensuring models are seamlessly transitioned from development to production.
Model Deployment & Infrastructure
Build and manage scalable cloud-based infrastructure (GCP, Azure) for deploying machine learning models in production.
CI/CD pipelines for ML/NLP model deployments.
Experience with Kubernetes and Docker for containerization and orchestration.
Implement and maintain versioning, governance, and monitoring tools for models using MLOps tools such as MLFlow, Kubeflow, or DVC.
Ensure secure and compliant handling of sensitive data in production environments.
Pipeline Automation
Build and maintain robust automated pipelines for training, validation, deployment, and retraining of machine learning models.
Collaborate with data engineers and data scientists to create continuous, automated workflows for data preparation, model training, and evaluation.
Implement automated model retraining based on performance metrics and new data availability.
Monitoring & Maintenance
Develop and implement monitoring systems to track model performance in production environments, including setting up real-time alerts for model drift and performance degradation.
Optimize pipelines and models to ensure high availability, fault tolerance, and performance scalability.
Work with the team to troubleshoot production issues related to models and pipelines.
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