Job description

Come work at a place where innovation and teamwork come together to support the most exciting missions in the world! About the Role 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. What You’ll Do 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) What We’re Looking For Required Qualifications 15 years of experience in data engineering, platform engineering, or data architecture Deep expertise in distributed systems and cloud-native design, with strong knowledge of modern AI architectures (LLM serving, RAG, agentic orchestration) Experience building production AI systems with strong observability, reliability engineering, and cost controls Proven track record of building or transforming enterprise-scale data platforms / lakehouse architectures Strong hands-on expertise in: Data pipelines (batch and streaming) Distributed data processing systems Cloud platforms (AWS, GCP, or Azure) Experience with data modeling, governance, lineage, and quality frameworks Excellent executive communication skills, with the ability to drive decisions under ambiguity Hands-on architectural credibility, with the ability to go deep when needed and guide teams from concept to production Strong programming skills (Python, SQL, or equivalent) Solid understanding of security, access control, and enterprise data management principles Preferred Qualifications (Value-Adding Prior Experience) Experience supporting or enabling AI/ML or GenAI workloads Familiarity with vector databases, embeddings, or feature engineering pipelines Exposure to event-driven or real-time data architectures Experience working within complex enterprise application ecosystems (CRM, ERP, HR, integrations, etc.) Prior experience in product-based, high-scale, or global environments Why Join Us 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 .

Requirements

Must-haves

Come work at a place where innovation and teamwork come together to support the most exciting missions in the world!
What You’ll Do
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)
What We’re Looking For
15 years of experience in data engineering, platform engineering, or data architecture
Deep expertise in distributed systems and cloud-native design, with strong knowledge of modern AI architectures (LLM serving, RAG, agentic orchestration)
Experience building production AI systems with strong observability, reliability engineering, and cost controls
Proven track record of building or transforming enterprise-scale data platforms / lakehouse architectures
Strong hands-on expertise in: Data pipelines (batch and streaming)
Distributed data processing systems
Cloud platforms (AWS, GCP, or Azure)
Experience with data modeling, governance, lineage, and quality frameworks
Excellent executive communication skills, with the ability to drive decisions under ambiguity

Nice-to-haves

Preferred Qualifications (Value-Adding Prior Experience)

Application prep

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Keywords to mirror

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Platform: workday
Effort: long

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