Lead Consultant – Enterprise AI Platforms
2 months ago
Job Title : Lead Consultant – Enterprise AI Platforms
Career Level – E
Company
AstraZeneca is a global, innovation-driven biopharmaceutical business that focuses on discovering, developing, and commercialising prescription medicines for some of the world's most serious diseases. But we're more than one of the world's leading pharmaceutical companies. At AstraZeneca, we're proud to have a unique workplace culture that inspires innovation and collaboration. Here, employees are empowered to express diverse perspectives and feel valued, energized and rewarded for their ideas and creativity.
Role
We are seeking a Senior AI Platform Engineer to join our Enterprise AI platform team at AstraZeneca. The ideal candidate will have excellent experience working with Kubernetes and KubeFlow, where they devised and deployed large-scale production infrastructure and platforms for scientific use cases (we will acknowledge expertise with other industries). The position will involve applying these skills to some of the most exciting machine-learning problems in drug discovery.
The successful candidate will be part of a new, collaborative team of multidisciplinary engineers and have the chance to create tools that will advance the standard of healthcare, improving the lives of millions of patients across the globe. Our data science environments will support major AI initiatives such as clinical trial data analysis, knowledge graphs, patient safety systems, deep learning-led drug discovery, and software as a medical device for our therapy areas. You will also have a responsibility to help provide the frameworks for data scientists to develop scalable machine learning and predictive models with our growing data science community in a safe and robust manner.
As a strong software developer with an interest in building complex systems, you will be responsible for inventing how we use technology, machine learning, and data to enable AstraZeneca's productivity. You will help design, build, deploy, and develop our next generation of data engines and tools at scale. You will bridge the gap between science and engineering and function with deep expertise in both worlds.
You will have the opportunity to learn many cutting-edge technologies around Machine Learning Platforms. You will push the boundaries to test, develop and implement new ideas, technology and opportunities.
Key Accountabilities
Collaborate closely with data science teams to design, deploy and manage the Kubernetes platform for Machine Learning. Provide the necessary infrastructure and platform to support the deployment and monitoring of ML solutions in production. Optimizing solutions for performance and scalability. Deployment of systems, applications, and tooling for data science on AWS cloud environments. Liaise with BTG data scientists to understand their challenges and work with them to help productionise ML pipelines, models and algorithms for innovative science. Take responsibility for all aspects of software engineering, from design to implementation, QA and maintenance with the support from ML experts. Liaise with other teams to enhance our technological stack, to enable the adoption of the latest advances in Data Processing and AIRequirements
12+ years’ or equivalent experience architecting and managing large Kubernetes clusters Experience of managing service mesh, such as Istio Experience of Kubernetes ML platforms and toolkits (Kubeflow) Knowledge on Linux/Shell scripting Certified Kubernetes Administrator/Developer Experience of scheduling strategies on clusters with different node types Modern DevOps mindset, using best of breed DevOps toolchains, such as Docker, Git, Jenkins Experience with infrastructure as code technology such as Ansible, Terraform and Cloud Formation Experience managing and automating real-world platforms/applications on AWS Strong software coding skills, with proficiency in Python, however exceptional ability in any language will be recognized. Experience with system monitoring tools such as Grafana, Prometheus, Thanos, etc Experience with Continuous Integration and the building of continuous delivery pipelines, such as: Helm, ArgoCDOther Desirable Skills
Experience with open-source and cloud-native Machine Learning Platforms and Toolkits Demonstrable knowledge of building MLOps environments to a production standard Understanding of Kubernetes internal networking and its effect on the performance of multi-node GPU ML training Experience in declarative management of Kubernetes objects using tools such as: kustomize Multi-cloud experience (AWS/Azure/GCP) Data storage experience with RDBMS and NoSQL technologies Experience in mentoring, coaching and supporting less experienced colleagues and clients. Experience with SAFe agile principles and practices Certified Kubernetes AdministratorDate Posted
15-Apr-2024Closing Date
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