Vantive
Website:
vantive.com
Company:
https://www.linkedin.com/company/vantivehealth
Seniority: Not Applicable
Industries: Medical Equipment Manufacturing
Job details:
Vantive is a vital organ therapy company on a mission to extend lives and expand possibilities for patients and care teams everywhere. For 70 years, our team has driven meaningful innovations in kidney care. As we build on our legacy, we are deepening our commitment to elevating the dialysis experience through digital solutions and advanced services, while looking beyond kidney care and investing in transforming vital organ therapies. Greater flexibility and efficiency in therapy administration for care teams, and longer, fuller lives for patients— that is what Vantive aspires to deliver.
We believe Vantive will not only build our leadership in the kidney care space, it will also offer meaningful work to those who join us. At Vantive, you will become part of a community of people who are focused, courageous and don’t settle for the mediocre. Each of us is driven to help improve patients’ lives worldwide. Join us in advancing our mission to extend lives and expand possibilities.
The AI Platform Architect defines, builds, and operates Vantive’s enterprise AI platform. This role is accountable for the end‑to‑end AI platform strategy, ensuring AI capabilities are secure, reliable, scalable, cost‑effective, and responsibly governed. The role sets the technical direction for enterprise AI, establishes standards and reference architectures, and enables teams across the organization to safely design, deploy, and operate AI systems across the full product lifecycle. The AI Platform Architect and Lead partners closely with Business, Cyber Security, Enterprise Architecture, and Risk & Compliance to align AI platform capabilities with business outcomes and long‑term strategy.
Key Responsibilities
Platform Strategy & Leadership
- Own the vision, roadmap, and lifecycle of the enterprise AI platform.
- Define and maintain platform standards, reference architectures, guardrails, and quality processes.
- Set technical direction for enterprise AI capabilities and influence broader product and technology strategies.
- Provide architectural leadership for AI/ML and agentic systems, including RAG and Graph RAG pipelines, knowledge‑graph‑enhanced retrieval, and LLM‑powered applications.
Architecture, Scalability & Reliability
- Ensure the AI platform meets enterprise‑grade requirements for scalability, resilience, security, compatibility, and global availability.
- Define patterns for distributed operations, failover, disaster recovery, and capacity planning across AI workloads.
- Design long‑term scaling models using performance modeling and cost‑efficient architectural strategies.
- Establish reusable platform services, onboarding pathways, and integration practices to accelerate adoption and reduce duplication.
Operational Excellence & Governance
- Own and track platform KPIs, SLAs, and SLOs (e.g., availability, latency, success rates, cost per inference).
- Implement platform‑wide observability, monitoring, and cost/capacity guardrails.
- Drive operational excellence through continuous measurement, reliability engineering, and improvement.
- Establish and enforce engineering quality and Responsible AI standards, including architecture reviews, automated testing, LLM evaluation, bias and fairness assessment, and model governance.
Collaboration & Stakeholder Engagement
- Partner with Business Stakeholders, Cyber Security, Enterprise Architecture, Risk & Compliance, and strategic vendors.
- Drive cross‑organizational alignment on AI standards, security controls, and responsible AI guardrails.
- Influence teams to adopt reference architectures and consistent release and change management practices.
AI Lifecycle Management
- Define and own a cohesive lifecycle strategy for AI and agentic systems, including:
- Continuous evaluation (automated and human‑in‑the‑loop)
- Feedback loops and ongoing monitoring
- Scheduled or event‑driven retraining
- Reinforcement learning from human and AI feedback, where appropriate
- Ensure AI systems remain accurate, safe, reliable, and cost‑effective over time.
Key Experiences & Attributes
- Bachelor’s degree in Computer Science, Engineering, Machine Learning, or a related field (Master’s preferred).
- Extensive experience defining AI/ML platform architectures, technical strategies, and multi‑year roadmaps.
- Deep understanding of modern AI patterns, including:
- Agentic and multi‑agent architectures
- Retrieval Augmented Generation (RAG) and Graph RAG
- Vector search, embeddings, and retrieval systems
- AI safety, evaluation, and guardrails
- Strong knowledge of cloud architectures, including identity and security, networking, containers/Kubernetes, platform governance, observability, and CI/CD.
- Proven ability to define engineering standards, lead architecture reviews, and drive adoption of secure, scalable AI platforms.
- Experience working in Agile delivery environments.
Reasonable Accommodation
Vantive is committed to working with and providing reasonable accommodations to individuals with disabilities globally. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application or interview process, please click on the link here and let us know the nature of your request along with your contact information. Form Link
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