Lead MLOps Engineer

hace 3 semanas

España Quantexa A tiempo completo

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 & InfrastructureBuild 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 AutomationBuild 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 & MaintenanceDevelop 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.Collaboration & LeadershipMentor 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.Required Skills & ExperienceProven 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.Automation & InfrastructureExpertise 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.Programming & FrameworksStrong 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.Strong knowledge of BDD/TDD, and general testing principles.Leadership & CollaborationExperience 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.Preferred ExperienceExperience with feature stores, embeddings, LLMs, and/or RAG architecture.Experience optimizing model inference for GPUs and deploying models with specialized hardware requirements.Familiarity with DevOps practices and tools for automating infrastructure and model deployments.Strong adherence to BDD/TDD development strategies.Experience or good understanding of parallelization concepts and HPC.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 missionWe 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 youQuantexa 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.
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