AI Engineer – Marketing Intelligence & Automation

SS&C is a leading provider of mission-critical, AI-powered technology and services empowering financial services and healthcare organizations to work smarter, faster, and securely. Founded in 1986, SS&C is headquartered in Windsor, Connecticut, and has offices worldwide. More than 23,000 financial services and healthcare organizations, from the world's largest companies to small and mid-market firms, rely on SS&C for expertise, scale, and technology.

Job Description

SS&C Intralinks | Marketing – Digital & Intelligence

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. You will design and deploy the agents, integrations, data pipelines, orchestration workflows, and intelligence services that turn fragmented marketing data and manual processes into connected, scalable systems.

This is not a traditional campaign execution role. You will build the engineering layer beneath an increasingly AI-enabled marketing organization: connecting platforms and data, enabling agents to reason over trusted information, automating workflows, and creating reusable services that can support personalization, attribution, predictive intent, conversational experiences, content operations, and executive intelligence.

You will partner closely with Digital Marketing, Marketing Operations, Demand Generation, Field Marketing, Content/Creative, Analytics, IT, Data, and other stakeholders. The initial focus is establishing a strong architecture and reusable foundation; over time, the role will help operationalize increasingly autonomous marketing capabilities.

• 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, retrieval, integrations, evaluation, observability, permissions, and orchestration—are reusable.
• Marketing teams spend less time assembling data and executing repetitive processes and more time interpreting insights, making decisions, and improving customer experiences.

• 4+ years of experience in AI engineering, software engineering, data engineering, ML engineering, marketing engineering, or a similar role building production technical systems.
• Strong programming skills in Python and experience with JavaScript/TypeScript or another modern application language.
• Hands-on experience building with LLMs, including APIs, tool/function calling, structured outputs, prompt design, agent frameworks, and production evaluation.
• Experience designing RAG or enterprise knowledge-retrieval systems, including embeddings, vector search, metadata, chunking, permissions, and source grounding.
• Experience building data pipelines, APIs, event-driven integrations, and workflow automations across SaaS and enterprise platforms.
• Working knowledge of cloud architecture and data platforms; experience with AWS and Amazon Redshift is particularly relevant.
• Understanding of data modeling, identity resolution, data quality, observability, security, access controls, and governance.
• Experience with marketing technology, CRM, CMS, analytics, digital experience, or marketing automation platforms is strongly preferred.
• Systems thinker who favors durable architecture and reusable services over isolated scripts or one-off solutions.
• Able to translate ambiguous business problems into technical designs, prototypes, production systems, and measurable outcomes.
• Clear communicator who can explain architecture, tradeoffs, risk, and business impact to both technical and non-technical partners.
• Comfortable testing emerging AI capabilities while applying appropriate controls for accuracy, privacy, security, and brand risk.
• Experience in B2B, enterprise software, financial technology, or regulated environments is a plus.

• Python; JavaScript/TypeScript; REST/GraphQL APIs; webhooks and event-driven integration patterns
• LLM APIs and agent frameworks; RAG; vector databases/search; model and prompt evaluation; observability
• AWS services and cloud-native application patterns; Amazon Redshift; SQL and modern data transformation/orchestration approaches
• CRM and marketing automation integrations, CMS/publishing APIs, web analytics, and customer/digital data sources
• Git-based development, CI/CD, testing, logging, secrets management, role-based access, and production monitoring
• Structured data and metadata frameworks such as schema.org/JSON-LD are useful for digital/search-oriented use cases

• Build the intelligence layer, not another silo. Solutions should connect and strengthen the broader marketing ecosystem.
• Ground AI in trusted information. Accuracy, provenance, permissions, and data quality matter as much as model capability.
• Automate with intent. Use autonomy where it creates measurable value; retain human review where judgment, compliance, or brand risk requires it.
• Design for reuse. A successful solution should make the next use case easier to build.
• Measure everything. Agents and automations should have clear success criteria, telemetry, and feedback loops.
• Business outcomes over AI novelty. Technology choices should improve decision quality, customer experience, speed, efficiency, or revenue impact.

Marketing is moving from a collection of channels and tools toward a connected intelligence system. This role will help create the engineering foundation that allows SS&C Intralinks Marketing to use AI across measurement, digital experiences, content, demand generation, field marketing, and full-funnel engagement—without creating a new set of disconnected AI point solutions.

Unless explicitly requested or approached by SS&C Technologies, Inc. or any of its affiliated companies, the company will not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services.

Applications will be accepted on an ongoing basis until the position is filled.

SS&C Technologies is an Equal Employment Opportunity employer and does not discriminate against any applicant for employment or employee on the basis of race, color, religious creed, gender, age, marital status, sexual orientation, national origin, disability, veteran status or any other classification protected by applicable discrimination laws.