Please, apply in English.
We are looking for a Senior DevOps Engineer to join our customer AI Ops platform team in R&D IT. The ideal candidate will have excellent experience working with Kubernetes and AWS. 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 taking these skills and applying them 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 together 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, software as a medical device, for our therapy areas. You will also have 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 the productivity of the company. You will help design, build, deploy and develop our next generation of data engines and tools at scale. You will be bridging the gap between science and engineering and functioning with deep expertise in both worlds.
You will have the opportunity to learn many cutting edge technologies around Machine Learning Platform. You will push the boundaries, to test, develop and implement new ideas, technology and opportunities.
- Collaborate closely with data science teams to design, deploy and manage 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 R&D 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 AI
- Team recruitment, training provision and coaching
- 2+ years’ or equivalent experience architecting and managing large Kubernetes clusters
- Experience of managing service mesh, such as Istio
- 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, ArgoCD
- Experience of Kubernetes ML platforms and toolkits (Kubeflow)
Other 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 performance of multi-node GPU ML training
- Experience of 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 of mentoring, coaching and supporting less experienced colleagues and clients
- Experience of SAFe agile principles and practices
Senior DevOps Engineer - Kubernetes
Sigma Technology Embedded Solutions
Västra Götalands län, Sweden
Business Intelligence, Software development, Digitalisation
2021-12-31 med chans till förlängning
Sofia Petersson, email@example.com, 46793411284