AI Engineer – Marketing Intelligence & Automation

SS&C Technologies•Remote - Massachusetts, US, MA

4d•Onsite

About The Position

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.

Requirements

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.
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.

Nice To Haves

Experience with marketing technology, CRM, CMS, analytics, digital experience, or marketing automation platforms is strongly preferred.
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

Responsibilities

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.
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.
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.
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.
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.
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.
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.
Engineer data and AI services that improve multi-touch measurement, channel contribution analysis, pipeline intelligence, forecasting, and executive decision support.
Build systems that connect AI-assisted content creation, governance, CMS workflows, structured data, localization/personalization, and distribution while maintaining human review where required.
Support SEO/AEO/GEO systems that monitor traditional and AI search visibility, structured data, content performance, citations, and opportunities for optimization at scale.
Create test harnesses, observability, quality controls, and evaluation frameworks for prompts, agents, models, workflows, and digital experiences so deployments are measurable and reliable.
Codify successful patterns into components, templates, documentation, and playbooks that allow the broader Marketing organization to scale AI safely and consistently.