Senior AI Engineer, Marketing Systems

About the role

Senior AI Engineer, Marketing Systems

What You'll Do

Design, build, and deploy production agentic workflows against real marketing objectives, including research, enrichment, segmentation, content operations, and campaign and lifecycle automation

Integrate agents with Salesforce and the surrounding martech stack, respecting existing data models, object structures, and operational hygiene

Combine licensed data providers, public web sources, and internal first-party data into reliable, evaluated agent pipelines

Work alongside marketing stakeholders to translate ambiguous business goals into scoped, shippable automation

Build the evaluation and guardrail layer, including accuracy checks, human review points, and cost and latency controls, so marketers can trust the output

Document patterns and train internal teams so the capability sustains after the engagement ends

Required Experience

Demonstrated experience building AI and agentic capabilities inside a marketing organization. You have shipped agents or AI automation for a marketing, demand generation, or revenue team, and you already understand funnel mechanics, lead qualification and routing, ICP and account scoring, attribution, list hygiene, and how marketing and sales hand work back and forth.

Hands-on production work with Claude, including agentic patterns such as tool use, multi-step orchestration, structured outputs, retrieval, prompt and context engineering, and agentic coding workflows

Salesforce fluency at the practitioner level, covering the data model, API integration, and how marketing data lives and moves inside a CRM

Strong software engineering fundamentals in Python and/or TypeScript, API integration, and the ability to stand up and maintain services independently

Experience integrating third-party and licensed data providers, including entity resolution, deduplication, and enrichment quality

Comfort working embedded with a client team, operating with light supervision, and driving from a vague problem statement to a working system

Strongly Preferred

Healthcare, health tech, provider, or payer domain exposure. Value-based care familiarity is a significant advantage.

B2B marketing to provider organizations, health systems, or physician practices

Experience with the marketing automation layer adjacent to Salesforce, such as Marketo, HubSpot, or Pardot

Track record as the first AI engineer embedded in a non-technical function

What Success Looks Like

30 days: Grounded in the current manual research workflow, data sources, and Salesforce structure, with a first working agent prototype in front of stakeholders

60 days: Research agent running against real prospect volume, with measured accuracy and clear time savings against the manual baseline

90 days: Output trusted and adopted by the marketing team, a second use case scoped and underway, and patterns documented for internal reuse