Design and deploy AI-powered applications embedded within marketing and CRM infrastructure
Build and productionize LLM-driven workflows, agentic systems, and retrieval-augmented (RAG) architectures
Architect secure integrations across Salesforce, marketing automation platforms, data warehouses, and external APIs
Develop scalable orchestration frameworks for AI workflows (event-driven, API-based, and automation-triggered systems)
Implement monitoring, logging, evaluation, and guardrails for production AI systems
Establish AI governance, access controls, and reliability standards within marketing systems
Optimize performance, latency, and cost of AI-powered workflows
Partner cross-functionally with Marketing, Revenue Operations, and Engineering teams to translate business requirements into scalable AI applications

4+ years of experience in software engineering or AI engineering
Strong proficiency in Python and SQL
Experience building and deploying LLM-based applications in production environments
Experience working with LLM APIs, vector databases, embeddings, and RAG architectures
Experience integrating systems via APIs, webhooks, and event-driven frameworks
Experience deploying and maintaining systems in AWS, GCP, or Azure
Strong understanding of system design, security, and reliability in distributed environments
Direct experience working within or integrating with modern marketing and revenue technology stacks, including: Marketo (or similar marketing automation platforms such as HubSpot or Pardot)
Salesforce (CRM architecture, objects, workflows, APIs)
Tableau (or similar BI tools such as Looker or Power BI)
Clay (data enrichment and outbound automation workflows)
Familiarity with marketing lifecycle management, attribution workflows, lead routing logic, and revenue operations processes is highly advantageous.

AI Engineer – Marketing Operations

Key skills