As a Forward Deployed AI Engineering Manager on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers.
\nYou'll work with enterprise clients to understand their unique challenges, lead a team that architects specific AI solutions, and ensure successful deployment and adoption of AI systems in production environments.
\nThis is a Management role that combines deep engineering and AI expertise, leading a team, and working on customer-facing problems. You'll work directly with customer engineering teams to integrate AI into their critical workflows.
\nCustomer Integration & Deployment
\nPartner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements.
\nDesign and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs).
\nBuild robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows.
\nDeploy and configure AI models and agents within customer security and compliance boundaries.
\nAI Agent Development
\nDevelop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation.
\nArchitect multi-agent systems that orchestrate between different models, tools, and data sources.
\nImplement evaluation frameworks to measure agent performance and iterate toward business objectives.
\nDesign human-in-the-loop workflows and feedback mechanisms for continuous agent improvement.
\nPrompt Engineering & Optimization
\nCreate sophisticated prompt engineering strategies optimized for customer-specific domains and data.
\nBuild and maintain prompt libraries, templates, and best practices for customer use cases.
\nConduct systematic prompt experimentation and A/B testing to improve model outputs.
\nImplement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate.
\nLeadership & Collaboration
\nServe as the Engineering Manager and technical point of contact for strategic enterprise accounts.
\nLead a team that is collaborating with customer data scientists, ML engineers, and software developers to ensure smooth integration.
\nWork closely with Scale's product and engineering teams to translate customer needs into product improvements.
\nDocument technical architectures, integration patterns, and best practices.
\nProblem Solving & Innovation
\nDebug complex technical issues across the entire stack, from data pipelines to model outputs.
\nRapidly prototype solutions to unblock customers and prove out new use cases.
\nStay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems.
\nIdentify opportunities for productization based on common customer patterns.
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\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
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\nWe work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid's network covers 12,000 financial institutions across the US, Canada, UK and Europe.
\nThe AI Applications Team You will have the opportunity to join as one of the founding members of this newly formed team that is dedicated to consolidating and rapidly scaling our successful bets so far, and grow with the team in our quest to accelerate Plaid's transformation into an AI-first company.
\nIn this role you will lead projects that enable and scale our business with our largest AI customers and partners, starting with personal finance use cases and expanding into many others; examples include:
\nAdditionally you will be expected to scale and extend our existing successful bets on AI-powered customer experience; examples include:
\nYou will have a front row seat to all the latest industry developments. Over time, with the skills and experience you develop and hone on this team, you can become an influential voice in defining where AI < Fintech will be heading longer term.
\nResponsibilities
\nRequirements
\nNice-to-Haves
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\nPlaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use.
\nResponsibilities
\nQualifications
\nNice to Have
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\nLLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.
\nDevelop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
\nImplement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.
\nEvaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.
\nEstablish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.
\nRetrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.
\nBuild document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.
\nOptimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.
\nML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.
\nBuild and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.
\nImplement model performance monitoring and data drift detection to ensure production models remain accurate over time.
\nSupport model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.
\nBackend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.
\nWrite high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.
\nInstrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.
\nCollaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.
\nContribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.
\nHelp grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.
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