AI Engineer - Marketing Intelligence & Automation

SS&C Intralinks is building an AI-enabled marketing intelligence model that connects data, digital experiences, automation, and AI to improve how Marketing understands customers, makes decisions, and activates across the funnel. We are hiring an AI Engineer to help build the technical foundation behind that model. This role sits at the intersection of AI engineering, marketing technology, data engineering, and digital experience.

• AI agents and agentic workflows:
  Design, build, evaluate, and maintain AI agents that can retrieve trusted marketing and business context, analyze information, recommend actions, and execute approved workflows across marketing systems.
• Marketing intelligence infrastructure:
  Build the services and pipelines that unify first-party marketing and CRM data, website behavior, campaign performance, search/AI visibility, intent signals, attribution inputs, and other relevant sources into an accessible intelligence layer.
• API and platform integrations:
  Connect marketing, data, CMS, CRM, analytics, AI, and workflow platforms through secure APIs, event-driven integrations, and reusable services rather than brittle one-off automations.
• Retrieval and knowledge systems:
  Create RAG and related retrieval patterns that allow AI systems to work from approved messaging, product information, campaign assets, customer insights, brand standards, and other governed enterprise knowledge.
• Predictive and next-best-action capabilities:
  Help develop models and decisioning services that combine first-party behavior and third-party signals to identify in-market accounts, prioritize opportunities, and recommend appropriate next actions.
• Digital journey orchestration:
  Build the technical capabilities required for adaptive website and digital experiences, including profile- and account-aware messaging, content recommendations, calls-to-action, and journey logic.
• Conversational AI:
  Develop LLM-powered experiences that can engage and qualify visitors, answer questions using approved enterprise knowledge, route inquiries, and integrate with downstream sales and marketing workflows.
• Measurement, attribution, and forecasting:
  Engineer data and AI services that improve multi-touch measurement, channel contribution analysis, pipeline intelligence, forecasting, and executive decision support.
• Content and publishing automation:
  Build systems that connect AI-assisted content creation, governance, CMS workflows, structured data, localization/personalization, and distribution while maintaining human review where required.
• Search and AI visibility automation:
  Support SEO/AEO/GEO systems that monitor traditional and AI search visibility, structured data, content performance, citations, and opportunities for optimization at scale.
• Experimentation and evaluation:
  Create test harnesses, observability, quality controls, and evaluation frameworks for prompts, agents, models, workflows, and digital experiences so deployments are measurable and reliable.
• Reusable engineering standards:
  Codify successful patterns into components, templates, documentation, and playbooks that allow the broader Marketing organization to scale AI safely and consistently.

The AI Engineer will provide a shared technical foundation for several planned Marketing 4.0 capabilities. The role is not expected to own each business program independently; instead, it will build the architecture, services, integrations, and AI components that make these capabilities possible.

• AI-driven journey orchestration and website personalization
• Hyper-personalized event and account intelligence
• AI-driven multi-touch attribution and forecasting
• Predictive intent and next-best-action routing
• Conversational AI and increasingly autonomous inbound qualification
• Content personalization and production systems at scale

• Marketing data and knowledge can be accessed by AI systems through governed, well-documented interfaces rather than manual exports and disconnected point solutions.
• High-value workflows move from manual execution to reliable human-in-the-loop or autonomous orchestration where appropriate.
• Agents produce traceable, measurable outputs grounded in trusted enterprise data and content.
• Digital, campaign, search, intent, attribution, and pipeline signals can be connected to produce actionable intelligence rather than isolated reporting.
• New AI use cases can be deployed faster because core components—identity,…