Offer description
Ardigen enables AI transformation for biotech and pharmaceutical companies to leverage the full potential of data. The company delivers value at the intersection of biology and computational methods to increase the likelihood of success and accelerate the drug discovery process. With our platforms based on advanced algorithms and state-of-the-art technology, we help researchers get scientific insights from a large amount of data, leading to new discoveries and breakthroughs in fields such as personalized medicine and drug development.
Let's grow together & code against cancer!
Key responsibilities
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Containerization & Orchestration: Spearhead the containerization of applications and services using Docker, Kubernetes, and other modern orchestration platforms.
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Collaboration with Cross-functional Teams: Support software engineers, data scientists, and bioinformaticians in deploying web applications and computational pipelines, including MLOps workflows for model training and deployment.
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CI/CD Pipeline Development: Build and maintain efficient CI/CD pipelines using tools like GitLab CI or CircleCI, ensuring rapid and reliable application deployment.
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Cloud Infrastructure Management: Design and manage cloud infrastructure using Infrastructure-as-Code (IaC) tools like Terraform or AWS CloudFormation on platforms like AWS, Google Cloud, or Azure.
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Architecture & Tech Selection: Collaborate on architecture decisions and technology selection for new features and modules, ensuring scalability, performance, and security.
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Security & Compliance: Implement DevSecOps best practices to manage system security, including access control, encryption, and regular security audits.
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Monitoring & Incident Management: Set up and manage proactive monitoring and alerting systems using tools like Datadog, New Relic, or Splunk to ensure smooth system operation and quick resolution of issues.
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Documentation: Document system configurations, architecture designs, and operational procedures to ensure clear communication and system maintenance.
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MLOps Automation: Collaborate on the automation of machine learning workflows using MLOps tools to streamline the deployment and monitoring of ML models.
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Problem Solving & Innovation: Take initiative in identifying and resolving technical challenges, staying updated on the latest trends and best practices in DevOps and MLOps.
YOU WILL GET BONUS POINTS FOR:
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Certifications: Professional-level certifications in AWS, Azure, or Google Cloud.
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Cloud Innovation: Experience collaborating with cloud providers to test and evaluate new services, and define technical requirements.
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MLOps Tools: Familiarity with MLOps tools and workflows, such as Kubeflow, MLflow, or Airflow, for automating machine learning pipelines.
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DevSecOps: Experience with integrating security best practices into the DevOps pipeline (DevSecOps), including vulnerability assessments and access controls.