Platforms Infrastructure Engineer
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Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. The Platforms Infrastructure team is seeking a driven individual to join us in addressing our evolving infrastructure challenges. At Dataiku, our mission is to architect, deploy, and maintain robust data platforms centered around our core product. We also provide industrialization and production configurations for mission-critical web services.
As domain experts, we proactively develop and disseminate reference infrastructure, best practices, knowledge, and tooling across all technical teams at Dataiku. This role encompasses a broad spectrum of responsibilities, from leveraging high-level managed services at hyperscalers to performing in-depth, low-level Linux debugging.
As a key contributor to the deployment of our core product, the team actively participates in development initiatives related to infrastructure and Linux system interactions.
We foster a collaborative environment and seek a like-minded individual to engage in highly collaborative projects. Effective communication skills are essential for interacting with diverse teams throughout the company.
This position is based in Paris and may be considered for remote work.
Your responsibilities:
- Design, deploy, and manage our core product based internal data platforms.
- Design, deploy, and configure production environments for multiple web services.
- Collaborate with teams to architect technical solutions, and subsequently deploy and manage those solutions.
- Develop core product features around cloud services and Linux integration.
- Guarantee optimal service capacity and robust security for all running workloads and services.
The role might be a good fit if you have:
- Proficiency in infrastructure automation using tools such as Terraform.
- Expertise in container technologies and managed Kubernetes clusters.
- Advanced scripting skills in Python.
- Field experience with Linux operating systems and low-level system troubleshooting
- Practical experience in networking and compute within a major cloud provider (AWS, Azure, or GCP).
- Demonstrated resilience in resolving complex technical challenges, with a relentless pursuit of root cause analysis.
Bonus point for any of these:
- Experience in implementing authentication and authorization systems, including LDAP, SAML, and OAuth2.
- Experience with configuration management tools as Ansible, Puppet or Chef.
- Knowledge of additional programming languages, particularly Go, considered a strong asset.
- Experience in networking and compute across multiple cloud providers (AWS, Azure, or GCP).
At Dataiku, you'll be part of a journey to shape the ever-evolving world of AI. We're not just building a product; we're crafting the future of AI. If you're ready to make a significant impact in a company that values innovation, collaboration, and your personal growth, we can't wait to welcome you to Dataiku!
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Dataiku
View Company ProfileDataiku is a massive, highly disruptive enterprise AI and machine learning platform fundamentally designed to democratize data science and bring "Everyday AI" to global organizations. Founded in 2013 by Florian Douetteau, Clément Stenac, Marc Batty, and Thomas Cabrol in Paris (and now headquartered in New York City), the company operates as the ultimate collaborative digital operating system for data teams. Under the hood, Dataiku seamlessly bridges the gap between advanced data scientists, data engineers, and business analysts—providing a unified, end-to-end environment for data preparation, predictive modeling, MLOps, and cutting-edge Generative AI integration. Their primary target audience spans massive Fortune 500 enterprises (including industry heavyweights like Unilever, GE, and BNP Paribas) who desperately need to break down data silos, scale their AI initiatives, and transition from clunky legacy analytics to fully automated, predictive data pipelines. What sets Dataiku apart in the fiercely competitive AI landscape is its uncompromising commitment to visual, low-code/no-code collaboration; completely empowering non-technical domain experts to build robust machine learning models while still offering deep, code-first flexibility for elite engineering teams.
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