Job description

A health technology organization is seeking a Senior AI Engineer to build production AI agents for marketing and revenue workflows. This senior, hands-on position will work across product, engineering, and marketing to convert business needs into reliable automation.

The initial focus is a top-of-funnel research agent that combines public internet research, licensed data, and internal information to identify, enrich, and prioritize prospective provider organizations. Results will integrate with Salesforce so marketing and sales teams can use the information within their existing workflows.

Responsibilities

• Design, build, and deploy AI agents for marketing research, enrichment, segmentation, content operations, campaign automation, and customer lifecycle workflows.
• Develop a top-of-funnel research agent that identifies, enriches, and prioritizes potential provider organizations.
• Combine public web information, licensed third-party data, and internal data within reliable agent workflows.
• Integrate AI agents with Salesforce and related marketing technology systems.
• Work with marketing stakeholders to turn loosely defined objectives into scoped, production-ready solutions.
• Build evaluation methods, accuracy checks, human review steps, and operational guardrails.
• Monitor and manage agent performance, cost, latency, and output quality.
• Address entity resolution, deduplication, list hygiene, and data enrichment quality.
• Develop and maintain supporting services using Python or TypeScript.
• Document reusable implementation patterns and train internal teams to maintain and expand the capability.
• Help identify and prioritize additional AI agent opportunities across marketing and revenue operations.

Required experience and skills

• Senior-level experience building and deploying AI agents or AI automation for marketing, demand generation, or revenue teams.
• Demonstrated understanding of marketing funnels, lead qualification, lead routing, ideal customer profiles, account scoring, attribution, and sales handoffs.
• Production experience with Claude.
• Experience with AI agent patterns, including tool use, multistep orchestration, structured outputs, retrieval, prompt engineering, context engineering, and agent-assisted development.
• Practitioner-level Salesforce experience, including CRM data models, APIs, object structures, and marketing data flows.
• Strong software engineering skills using Python or TypeScript.
• Experience developing API integrations and independently maintaining production services.
• Experience integrating public, internal, licensed, and third-party data sources.
• Experience with entity resolution, deduplication, enrichment, and data-quality controls.
• Ability to establish evaluation frameworks, human review processes, and agent guardrails.
• Experience working directly with business stakeholders and delivering solutions with limited supervision.
• Ability to move from an ambiguous business problem to a functional, production-ready system.
• Healthcare, health technology, provider, payer, or value-based care experience is strongly preferred.
• Experience supporting business-to-business marketing for provider organizations, health systems, or physician practices is strongly preferred.
• Familiarity with marketing automation platforms connected to Salesforce, such as Marketo, HubSpot, or Pardot, is strongly preferred.
• Experience serving as the first AI engineer embedded within a nontechnical business function is strongly preferred.

FAQs

1. What does a Senior AI Engineer, Marketing Automation do?

A Senior AI Engineer in marketing automation designs and builds AI-powered systems that improve how marketing campaigns, customer interactions, and personalization workflows are executed. The role combines machine learning, generative AI, software engineering, marketing data, and automation to create scalable production solutions.

2. What types of AI use cases are common in marketing automation?

AI applications may include customer segmentation, content generation, personalization, lead scoring, campaign optimization, recommendation systems, and automated customer engagement. Engineers evaluate each use case based on data availability, business value, model performance, and the level of automation required.

3. How are LLMs used in marketing automation?

Large language models can support content generation, campaign variations, customer-message personalization, classification, summarization, and conversational experiences. Senior AI Engineers are responsible for designing reliable workflows around LLMs, including prompt management, context handling, evaluation, guardrails, and monitoring.

4. How does a Senior AI Engineer integrate AI with marketing platforms?

AI services may connect with CRM, customer-data, marketing automation, analytics, and campaign-management platforms through APIs and data pipelines. Engineers design integrations that securely move relevant customer and campaign data between systems while handling authentication, validation, errors, and data synchronization.

5. How does a Senior AI Engineer evaluate marketing AI models?

Evaluation depends on the use case and may include accuracy, relevance, conversion impact, engagement, latency, cost, and consistency. For generative AI applications, evaluation may also include factuality, output quality, safety, instruction adherence, and human feedback.

6. What role does customer data play in AI-powered marketing automation?

Customer data provides the signals needed for segmentation, personalization, prediction, and campaign optimization. Engineers must ensure that data pipelines are reliable and that AI workflows apply appropriate privacy, access-control, governance, and data-quality practices.

7. How does the role support production AI systems?

Senior AI Engineers develop deployment workflows, monitoring, evaluation pipelines, model or prompt versioning, logging, and performance controls for production systems. They also troubleshoot failures and optimize latency, infrastructure usage, model performance, and operational costs.

8. How does a Senior AI Engineer work with marketing and product teams?

The engineer translates marketing objectives and customer-experience requirements into technically feasible AI solutions. Collaboration may involve defining use cases, selecting models, establishing success metrics, designing experiments, reviewing outputs, and iterating based on campaign or user results.