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Senior DevOps Engineer
2 months ago
AI and digitization will deeply change healthcare in the coming years. AI is already making rapid advances in medicine, and that trend will only increase. At Amaranth, we use deep learning to analyze radiomic, histology, genomic (*omic) data to find biomarkers that have diagnostic value. Our goal is to inform better treatment, increase portability, and enhance predictive value by extracting latent information from already established modalities.
We are seeking an experienced Senior DevOps Engineer with 4-5+ years of hands-on experience in DevOps practices, automation, cloud infrastructure, and MLOps. As a key member of the engineering team, you will collaborate closely with both the development and ML teams to streamline and automate processes, ensure the reliability and scalability of our systems, and implement cutting-edge DevOps and MLOps practices. Your work will support continuous integration and continuous deployment (CI/CD) pipelines, infrastructure as code, and the orchestration of machine learning models from development to production.
We are looking for passionate, driven people who will enjoy working in a multi-disciplinary team of developers, physicians, pathologists, and ML researchers. There are technical challenges and social impact that you can look forward to.
- CI/CD Pipeline Management: Design, implement, and maintain continuous integration and continuous delivery (CI/CD) pipelines to automate build, test, and deployment processes.
- Infrastructure as Code (IaC): Develop and manage scalable, secure infrastructure using tools like Terraform, CloudFormation, or Ansible, ensuring efficient deployment and infrastructure consistency across environments.
- Cloud Management: Deploy, manage, and optimise applications in cloud environments (AWS, Azure, GCP), with a focus on cost-efficiency, availability, and security.
- Containerization and Orchestration: Manage containerized applications using Docker, and orchestration tools like Kubernetes, EKS, or AKS to deploy, scale, and manage clusters.
- Monitoring and Logging: Set up and maintain monitoring, alerting, and logging solutions using tools like Prometheus, Grafana, ELK Stack, or Datadog to ensure proactive detection and resolution of issues.
- Automation and Scripting: Develop automation scripts and tools to reduce manual effort and improve process efficiency using scripting languages like Python, Bash, or PowerShell.
- Security and Compliance: Implement security best practices, such as secrets management, vulnerability scanning, and automated compliance checks, throughout the DevOps pipeline.
- Incident Response: Manage and respond to production incidents, ensuring quick resolution and minimal downtime. Conduct post-incident analysis and implement preventive measures.
- Model Deployment and Management: Implement and manage scalable, automated pipelines for deploying machine learning models in production, optimise ML workflows, model packaging, and resource allocation during training and inference.
- Certifications: AWS Certified DevOps Engineer, Google Cloud Professional DevOps Engineer, or Azure DevOps certification.
- Experience with Serverless Architectures: Familiarity with serverless technologies (AWS Lambda, Azure Functions).
- Database Management: Experience with managing and scaling databases (e.g., MySQL, PostgreSQL, MongoDB).
- Security and Compliance: Knowledge of security best practices, including IAM, encryption, and audit logging, with a specific focus on securing machine learning models.
- Familiarity with Distributed Training: Experience in optimising and scaling distributed training of machine learning models using tools like PyTorch Distributed.
