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Staff DevOps Engineer (Linux)

Quantifind
CompanyQuantifind
CategoryEngineering
LocationWashington
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted9 Jun 2026
Last verified30 Jul 2026
SourceEmployer career page (greenhouse)
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Description
Who You Are You embrace the ethic of “infrastructure as code” to provide consistency and security for both data center and IT deployments. You deliver solutions that are scalable, maintainable and follow the ethos of “cattle, not pets.” You hold yourself and your teammates to a high standard when writing clean, performant, and maintainable code for deployments and configuration management. You enjoy finding new ways to improve the design, performance, and testing of the systems and services that you deploy and operate.  You work well with cross-functional teams that include platform engineers, data scientists, security engineers and product managers. You care deeply about your work and its impact on your team’s productivity. You have a great work ethic and are able to take on major projects from start to finish, collaborating with other engineers. You value the security of a company with a proven business model. On a personal note, you are fun to work with; you maintain a positive attitude throughout the Agile Sprint cycle. Who We Are Quantifind helps some of the world’s biggest banks catch money laundering and fraud. Quantifind also works with government agencies to use the same platform to uncover criminal networks and combat money laundering committed by internationally sanctioned entities. Unlike other players in this space, Quantifind delivers results as software-as-a-service (SaaS) with consumer-grade user experiences.  Quantifind is a data science technology company whose AI platform uncovers signals of risk across disparate and unstructured text sources. In financial crimes risk management, Quantifind’s solution uniquely combines internal financial institution data with public domain data to assess risk in the context of Know Your Customer (KYC), Customer Due Diligence (CDD), Fraud Risk Management, and Anti-Money Laundering (AML) processes. Today these compliance processes are burdened by ever-increasing regulatory responsibilities and an expectation of frictionless transactions. Legacy technologies demand increasingly more human resources as the operations expand; Quantifind’s SaaS solution offers a way to cut through the inefficiency and enhance effectiveness simultaneously.  To help you succeed, we provide a supportive environment that fosters collaboration between teams and team members, where learning and professional growth are considered a key part of your success, and of ours. We offer a flexible work environment with a family friendly work-life balance. What a Great Candidate Looks Like: 8+ years of professional industry experience, in addition to your academic experience Strong professional development experience using Python or Go Experienced in Linux administration with a preference for Ubuntu package management Experience with Docker and Kubernetes for application development and deployment. Experience with network, host and application security practices both on prem and in the Cloud Strong experience with modern (on-prem/cloud) fleet management tools Experience with monitoring, observability, availability engineering, and the art of performance metrics Experience with release engineering tools like Maven, Jenkins, etc. Experience with Apache Airflow for orchestration Experience with uptime/availability engineering, performance metrics, and monitoring Familiarity with network design, operation and troubleshooting Excellent communication, presentation and collaborative problem-solving skills Great team player who has a strong desire to change the way people use their data and have a huge impact on a small team The Opportunity We Offer As a Staff DevOps Engineer, you will play a critical role in managing the infrastructure for Quantifind’s systems. Our stack includes fleet management in an IT fashion, fleet management in a Datacenter fashion, back-end services and infrastructure, machine learning modules surfaced through
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