As a Specialist Solutions Architect (SSA),AI Tooling & System Management, you will build and manage the AI tooling stack and system infrastructure that empowers Field Engineering to deliver customer outcomes with higher velocity.
\nThese capabilities will be utilized by our Go-To-Market teams, including Solutions Architects and Account Executives, to accelerate technical demos, proofs of concept, and customer engagements.
\nYou will bring consistency to our internal AI tooling stack, establish standards for AI-driven development practices, and scale these capabilities across the department.
\nA critical aspect of this role is building the infrastructure that enables agent networks to perform with high quality and reliability,including context management systems, data integrations, and supporting tooling.
\nAdditionally, you will develop internal applications and technical tools that enhance the overall lifecycle, track adoption metrics to measure impact, and partner with stakeholders to drive continuous improvement through intelligent automation and AI-augmented workflows.
\nThe impact you will have:
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_946354fd-05b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8409019002","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$180,000-$247,500 USD","x-skills-required":["Cloud Platforms & Architecture","AI Tooling","Context Management & Agent Networks","Application Development","Metrics & Analytics","System Integration & Data Pipelines","Security & Platform Administration","Infrastructure Automation & DevOps"],"x-skills-preferred":["Security","System Integrations & Application Deployment","Developer Experience & AI Tooling"],"datePosted":"2026-04-18T15:55:11.227Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Northeast - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Cloud Platforms & Architecture, AI Tooling, Context Management & Agent Networks, Application Development, Metrics & Analytics, System Integration & Data Pipelines, Security & Platform Administration, Infrastructure Automation & DevOps, Security, System Integrations & Application Deployment, Developer Experience & AI Tooling","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":247500,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_77ff2013-8f9"},"title":"Senior Product Manager, Context Engineering","description":"ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. As a Senior Product Manager, Context Engineering, you'll be surrounded by teammates who care deeply, challenge each other, and celebrate wins.
\nWith tools that amplify your impact and a culture that backs your ambition, you won't just contribute. You'll make things happen–fast.
\nThe Opportunity:
\nZoomInfo built the industry's most sophisticated GTM data acquisition infrastructure. Now we're applying that same rigor to context engineering,the emerging discipline that determines whether AI systems deliver transformative value or incremental improvement.
\nThis role architects the context layer powering our AI intelligence across Copilot, GTM Studio, and MarketingOS. You'll transform how ZoomInfo's agentic workflows access, compress, and deliver precisely the right information at exactly the right moment.
\nThe impact is organization-wide: every AI interaction, every intelligent recommendation, every autonomous agent action depends on the context infrastructure you’ll build.
\nWe've transitioned to AI-first product thinking company-wide. The context pipelines exist but remain nascent,creating a rare opportunity to define architectural patterns and platform standards that compound value across multiple product teams in the years to come.
\nWhat You'll Do:
\nArchitect Context Acquisition Pipelines
\nDesign and optimize how ZoomInfo retrieves, transforms, and delivers context from our semantic data layer, memory systems, and data producers. You'll balance retrieval quality against latency and cost constraints, implementing hybrid search strategies, intelligent caching, and context compression techniques that maintain information density while respecting token budgets.
\nOwn the Context Layer Platform
\nBuild infrastructure serving multiple product teams,Copilot, GTM Studio, MarketingOS,as internal customers. Establish API contracts, developer experience standards, and integration patterns that accelerate feature velocity.
\nMaintain the delicate balance between providing flexible building blocks and opinionated solutions that encode best practices.
\nDrive Quality Through Measurement
\nImplement evaluation frameworks using RAGAS metrics and custom benchmarks. Monitor retrieval precision, context relevance, hallucination rates, and system performance in production.
\nTranslate quality signals into architectural improvements, working closely with ML engineers to iterate on embedding models, reranking strategies, and retrieval algorithms.
\nNavigate Emerging Research
\nContext engineering evolves weekly. You'll continuously evaluate innovations,GraphRAG for multi-hop reasoning, test-time compute scaling, multimodal retrieval, compression techniques,determining which advances warrant production investment versus which remain academic curiosities.
\nBring external best practices to ZoomInfo while contributing learnings back to the broader community.
\nOrchestrate Cross-Functional Execution
\nTranslate between three distinct worlds: ML engineers optimizing retrieval algorithms, platform engineers building scalable infrastructure, and product teams shipping customer features.
\nEstablish communication cadences, prioritization frameworks, and decision-making processes that balance urgent requests against strategic platform development.
\nWhat You’ll Bring:
\nWho You Are:
\nTechnical Foundation
\nExpert-level understanding of RAG system architecture,you can discuss embedding dimensionality trade-offs, vector database indexing strategies, and reranking approaches with depth.
\nYou've built or significantly contributed to production retrieval systems, not just managed them at arm's length.
\nPython and SQL proficiency enables you to review code, analyze retrieval issues, and prototype solutions for concept validation.
\nPlatform Product Mindset
\nExperience building infrastructure products where internal engineering teams are your customers.
\nYou measure success through downstream product velocity improvements and developer satisfaction scores, not just uptime metrics.
\nYou understand platform economics,how each additional team using your infrastructure increases its value through shared learnings and amortized costs.
\nIntellectual Velocity
\nYou read recent research papers from arXiv, ACL, NeurIPS.
\nYou prototype emerging techniques to understand their practical constraints.
\nYou maintain strong opinions weakly held, updating your architectural assumptions as evidence accumulates.
\nThe discipline moves too fast for static expertise,continuous learning is non-negotiable.
\nStrategic Communication
\nYou translate between technical depth and business impact fluently.
\nYou can explain to executives why implementing GraphRAG takes 6 months but unlocks $10M in product capabilities.
\nYou can communicate to engineers why business constraints require shipping 'good enough' in 3 weeks rather than 'optimal' in 3 months.
\nYou influence without formal authority through data, clear reasoning, and earned credibility.
\nThe Environment:
\nReporting & Collaboration
\nReport to the Senior Product Director for Context Engineering, Semantic Data Layer, and Agentic Memory within ZoomInfo's Intelligence team.
\nWork alongside PMs responsible for signals and ML scoring/recommendation models.
\nTogether, you ensure our agentic workflows fill context windows with high-quality, information-dense content exactly when needed.
\nPace & Problems
\nFast-moving engineering team that understands the space.
\nCompany-wide AI adoption push creates both urgency and opportunity.
\nExpect interesting problems: How do we maintain sub-200ms retrieval latency at scale?
\nWhen does GraphRAG justify its indexing cost?
\nHow do we balance context freshness with cache efficiency?
\nYou'll shape answers that become architectural patterns across the organization.
\nImpact
\nDefine a nascent discipline at a company that's already AI-first in product thinking and organizational structure.
\nYour architectural decisions compound,every improvement to context quality multiplies across Copilot, GTM Studio, MarketingOS, and future products we haven't imagined yet.
\nThis is infrastructure work with direct line-of-sight to customer value.
\n#LI-PS1 #LI-remote
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_77ff2013-8f9","directApply":true,"hiringOrganization":{"@type":"Organization","name":"ZoomInfo","sameAs":"https://www.zoominfo.com/","logo":"https://logos.yubhub.co/zoominfo.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/zoominfo/jobs/8206116002","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$89,200-$133,800 USD","x-skills-required":["Product Management","ML/AI Infrastructure","RAG Systems","Vector Databases","Semantic Search","Context Management","Graph Databases","Context Compression","Embedding Models","Retrieval Evaluation Frameworks"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:44:52.232Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Waltham, Massachusetts, United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Product Management, ML/AI Infrastructure, RAG Systems, Vector Databases, Semantic Search, Context Management, Graph Databases, Context Compression, Embedding Models, Retrieval Evaluation Frameworks","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":89200,"maxValue":133800,"unitText":"YEAR"}}}]}