A complete application in a minute — tailored resume and cover letter, ready to send.
Qualys, Inc. is seeking an experienced leader to define and lead the reference architecture for third-party AI products within IT, enabling secure, scalable agentic capabilities and accelerating enterprise-wide AI adoption.
You will design, scope, and implement complex AI workflows across data, integration, and governance domains. You will own the end-to-end design and build of a modern lake house and AI ecosystem—powering intelligent automation, analytics, and global-scale AI use cases with
Come work at a place where innovation and teamwork come together to support the most exciting missions in the world!
You will define and lead the reference architecture for third-party AI products within IT, enabling secure, scalable agentic capabilities and accelerating enterprise-wide AI adoption. In this role, you will design, scope, and implement complex AI workflows, operating at the intersection of business operations, AI workflow design, data architecture, enterprise integration, data orchestration, and change management. You will own the end-to-end design and build of a modern lake house and AI ecosystem—powering intelligent automation, advanced analytics, and global-scale AI use cases. This includes working across structured, semi-structured, and unstructured data, while ensuring solutions are secure, reliable, scalable, and aligned with real-world clinical and administrative processes. You bring a hands‑on mindset, driving outcomes across architecture, engineering, and platform leadership.
Own the architectural vision, principles, and guardrails for AI-first capabilities, including agent orchestration, runtime hosting, model gateways, retrieval/grounding, and enterprise integrations. Define reference architectures for agentic runtimes, tool integration, policy enforcement, identity, and secure data access in production. Drive non-functional requirements (reliability, performance, cost efficiency, scalability) and establish SLOs and validation approaches. Translate business and operational requirements into scalable AI flow architectures that are grounded in customer context and AI best practices. Develop and optimize data ingestion, transformation, and orchestration pipelines across diverse enterprise systems Establish data models, governance standards, lineage, and data quality frameworks Enable AI readiness through structured data access, feature pipelines, and embedding/vector capabilities Design and implement secure, compliant, and scalable cloud-based data infrastructure Build APIs and reusable platform services for downstream AI and application teams Partner with engineering and business teams to translate requirements into robust data solutions Contribute to foundational MLOps/LLMOps readiness (pipeline standardization, monitoring, lifecycle considerations)
Opportunity to build the enterprise data and AI foundation from scratch High-impact, high‑visibility role influencing long‑term strategy Work on scalable, real‑world AI enablement
The salary range for this position is $185,000 - $220,000 per year.
Final compensation will be determined based on several factors, including but not limited to skills, relevant experience, and work location. Please note this range reflects base salary and does not include incentive compensation or potential equity grants.
We also offer a comprehensive and highly competitive benefits package.
Qualys is an Equal Opportunity Employer, please see our EEO policy.
Join our talent community and receive the latest Qualys news, content, and be first in line for new job opportunities.
Join our Talent Community!
Qualys, Inc. (NASDAQ: QLYS) is a pioneer and leading provider of disruptive cloud-based security, compliance and IT solutions with more than 10,000 subscription customers worldwide, including a majority of the Forbes Global 100 and Fortune 100. Qualys helps organizations streamline and automate their security and compliance solutions onto a single platform for greater agility, better business outcomes, and substantial cost savings.