Senior MLOps Engineer, Quantexa

• The role demands technical expertise in MLOps, experience in collaborating with remote teams
• Proven experience deploying machine learning models into production, including managing their lifecycle (deployment, monitoring, retraining)
• Hands-on experience with MLOps tools like MLFlow, Kubeflow, DVC, or Weights and Biases
• Strong background in cloud platforms (GCP)
• Proficiency in Kubernetes and Docker for managing containerized applications
• Expertise in automating CI/CD pipelines for machine learning workflows using tools like Jenkins, GitLab, or similar
• Proficiency in infrastructure as code using tools like Terraform or Helm for managing cloud resources
• Experience with setting up and managing GPU-accelerated environments for large-scale model inference
• Strong programming skills in Python and good knowledge of Scala and/or Java. Experience with ML libraries
• Solid understanding of distributed processing systems like Spark
• Experience building production grade APIs in Python
• Experience leading teams, mentoring engineers, and fostering collaboration within an agile framework (Scrum)
• Strong communication skills to coordinate effectively with teams across different time zones and geographies

Desirable

• 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 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
• 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
• 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
• 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
• Mentor junior MLOps engineers, providing guidance on technical challenges, best practices, and career development
• Collaborate closely with data scientists and machine learning engineers to ensure that models can be effectively transitioned from development to production
• Lead and participate in Scrum ceremonies, promoting a high-performance, collaborative environment with a focus on continuous improvement